NEUDM/semeval-2014 · Datasets at Hugging Face

task_type stringclasses 1 value	dataset stringclasses 1 value	input list	output stringlengths 19 428	situation stringclasses 1 value	instruction stringclasses 2 values
generation	semeval-2014	[ "The driver updates don't fix the issue, very frustrating." ]	[['driver updates', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "First, it does not have a push button to open the lid." ]	[['push button', 'negative'], ['lid', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I agree with the previous comment that ASUS TECH SUPPORT IS HORRIBLE WHICH IS A CON IN MY OPINION." ]	[['ASUS TECH SUPPORT', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The biggest problem is that the box had no instructions in it." ]	[['instructions', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Screen is awesome, battery life is good." ]	[['Screen', 'positive'], ['battery life', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It's not a wear-and-tear issue, not due to user carelessness and most importantly, they CAN'T guarantee the problem will be solved if it is sent for service and I have to accept the outcome." ]	[['service', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "We also use Paralles so we can run virtual machines of Windows XP Professional, Windows 7 Home Premium, Windows Server Enterprise 2003, and Windows Server 2008 Enterprise." ]	[['Paralles', 'neutral'], ['Windows XP Professional', 'neutral'], ['Windows 7 Home Premium', 'neutral'], ['Windows Server Enterprise 2003', 'neutral'], ['Windows Server 2008 Enterprise', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The first fell apart right after the 1-year-warranty." ]	[['1-year-warranty', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It seems they could have updated XP and done without creating Vista." ]	[['XP', 'positive'], ['Vista', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I wouldn't play a first-person shooter with this, mind, but if you wanted to run MS Office, email, chat, download a video, listen to music from a certain fruit-named music store, and were looking for a highly portable yet powerful netbook to do that all in, I'd highly recommend checking this out." ]	[['MS Office', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "and looks very sexyy:D really the mac book pro is the best laptop specially for students in college if you are not caring about price." ]	[['price', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The only thing I wish this had was the option to turn off the touchpad with a button like my big 16\" laptop does." ]	[['touchpad', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The only thing i can say is that the touch pad doesnt work like it should all the time." ]	[['touch pad', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "My husband uses it mostly for games, email and music." ]	[['games', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ " Until I bought the Dell, I thought you just looked for what you wanted (size, software, options, hardware) and purchase the best deal you could find." ]	[['size', 'neutral'], ['software', 'neutral'], ['hardware', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "After a little more than a year of owning my MacBook Pro, the monitor has completely died." ]	[['monitor', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "DO NOT BUY GATEWAY COMPUTERS THEY ARE JUNK AND THE WARRANTY COMPANY IS HORRIBLE." ]	[['WARRANTY COMPANY', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I could save ten essay papers and have hardly any memory left." ]	[['memory', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It even has a great webcam, and Skype works very well." ]	[['webcam', 'positive'], ['Skype', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The newer black keyboard took a little bit away from the previous gray one which looked really slick, but it is still a great notebook!" ]	[['black keyboard', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I picked it out because it was inexpensive ($400) and I thought it would be a good, easy to use first laptop." ]	[['use', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "wonderful features." ]	[['features', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Its ease of use and the top service from Apple- be it their phone assistance or bellying up to the genius bar- cannot be beat." ]	[['use', 'positive'], ['service', 'positive'], ['phone assistance', 'positive'], ['genius bar', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "my niece and nephew have played a few web games and it runs anything that doesn't require a dedicated video card." ]	[['video card', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "-4 RAM slots, 2 HDD Bays*, 16GB RAM support -No Wireless Issues, at least for me." ]	[['RAM slots', 'neutral'], ['HDD Bays', 'neutral'], ['16GB RAM support', 'neutral'], ['Wireless', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The computer itself was fast, ran smoothly, and had no problems." ]	[['ran', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "We carry the netbook around here and there, hence it's kinda of irritating when the LCD just \"slide\" downwards." ]	[['LCD', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "My Toshiba did not have sound on everything, just certain things." ]	[['sound', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Its pretty fast and does not have hiccups while I am using it for web browsing, uploading photos, watching movies (720p) on occasion and creating presentations." ]	[['web browsing', 'positive'], ['uploading photos', 'positive'], ['watching movies', 'positive'], ['creating presentations', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "While Apple's saving grace is the fact that they at least stand behind their products, and their support is great, it would be nice if their products were more reliable to justify the premium." ]	[['support', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It is absolutely horrible to use, despite all its so called advanced features." ]	[['features', 'negative'], ['use', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "After replacing the hard drive the battery stopped working (3 months of use) which was frustrating." ]	[['hard drive', 'neutral'], ['battery', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "This is a review of windows vista system." ]	[['windows vista system', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The brand is tarnished in my heart." ]	[['brand', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Boots up fast and runs great!" ]	[['Boots up', 'positive'], ['runs', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "After talking it over with the very knowledgeable sales associate, I chose the MacBook Pro over the white MacBook." ]	[['sales associate', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I love my Apple, it is quick and easy to use." ]	[['use', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Fully charged, the MacBook Pro can last about five hours unplugged." ]	[['charged', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I cannot be happier with the service or product." ]	[['service', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "the manufacturer's warranty only covers replacement/repair of parts." ]	[['warranty', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The best thing about this laptop is the price along with some of the newer features." ]	[['price', 'positive'], ['features', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Just keep in mind the graphics is not dedicated so loading the movie almost took a minute, but it ran fairly smooth for a non-dedicated graphics card." ]	[['graphics', 'conflict'], ['non-dedicated graphics card', 'conflict']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It's so much easier to navigate through the operating system, to find files, and it runs a lot faster!" ]	[['operating system', 'positive'], ['runs', 'positive'], ['navigate', 'positive'], ['find files', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The trackpad was easy to learn and navigate." ]	[['trackpad', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Games being the main issue." ]	[['Games', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "But to be honest, I don't use my computer for anything like graphics editing and complex data analysis and gaming." ]	[['gaming', 'neutral'], ['graphics editing', 'neutral'], ['complex data analysis', 'neutral']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "A month or so ago, the freaking motherboard just died." ]	[['motherboard', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "We upgraded the memory to four gigabytes in order to take advantage of the performace increase in speed." ]	[['memory', 'neutral'], ['speed', 'positive'], ['performace', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The keyboard is too slick." ]	[['keyboard', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Not even safe mode boots." ]	[['safe mode', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I have had another Mac, but it got slow due to an older operating system." ]	[['operating system', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I can barely use any usb devices because they will not stay connected properly." ]	[['usb devices', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The OS is also very user friendly, even for those that switch from a PC, with a little practice you can take full advantage of this OS!" ]	[['OS', 'positive'], ['OS', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The real stand out on this computer is the feel of the keyboard and it's speed." ]	[['keyboard', 'positive'], ['speed', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The technical service for dell is so 3rd world it might as well not even bother." ]	[['technical service for dell', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ ":-)If you buy this - don't go into it expecting 7 hrs of battery life, and you'll be perfectly satisfied." ]	[['battery life', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "Overall, this laptop is definitely a keeper with its simple yet stylish design and its array of fantastic colors to choose from." ]	[['design', 'positive'], ['colors', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "and the multiple page viewer ( allows you to press one button to see every separate page currently opened at the same time in one screen) are great for those who are working non stop or just shopping online." ]	[['multiple page viewer', 'positive']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "I was sorly disapointed to discover that HP (what I thought was a reputable company) wouldn't honor the warrenty when the fan blade fell apart." ]	[['warrenty', 'negative'], ['fan blade', 'negative']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "The mouse is a little bit different than what you're used to though- it has one big flat panel and one full bar (instead of two separate ones) to click with- but you get used to it quite quickly." ]	[['mouse', 'conflict']]	none	Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]
generation	semeval-2014	[ "It is far more popular as a bar than as a restaurant, with only a few tables and the waiter being the bartender, but we greatly enjoyed the unobtrusive atmosphere." ]	{'aspect_term': [['bar', 'positive'], ['tables', 'negative'], ['waiter', 'neutral'], ['bartender', 'neutral'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'negative'], [None, 'neutral'], [None, 'neutral'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The food was very well prepared." ]	{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The food is great and authentic." ]	{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "They are tasty, but I suggest only eating one with meat because they tend not to mesh that well with the average American digestive system." ]	{'aspect_term': [['meat', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "I went there for lunch and it was not as good as I expected from the reviews I read." ]	{'aspect_term': [['lunch', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way." ]	{'aspect_term': [['menu', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Good food at the restaurant (a bit expensive, but great if you want to impress your date)." ]	{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The ambience was so fun, and the prices were great, on top of the fact that the food was really tasty." ]	{'aspect_term': [['ambience', 'positive'], ['prices', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Service was decent, but not as smooth as I would expect from a place with these prices and reputation." ]	{'aspect_term': [['Service', 'conflict'], ['prices', 'negative'], ['reputation', 'positive']], 'aspect_category': [[None, 'conflict'], [None, 'negative'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The food looked very appetizing and delicious since it came on a variety of fancy plates." ]	{'aspect_term': [['food', 'positive'], ['plates', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The waiter was attentive." ]	{'aspect_term': [['waiter', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The hot and sour soup was unbearably hot and tasted of only pepper and nothing else." ]	{'aspect_term': [['soup', 'negative'], ['pepper', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Decent wine at reasonable prices." ]	{'aspect_term': [['wine', 'positive'], ['prices', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "I'd call it an 'italian dinner'." ]	{'aspect_term': [['dinner', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The pizza was really good." ]	{'aspect_term': [['pizza', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "We've been following chef Lyle's food around New York for 15 years and while remaining classic, his innovations with bistro fare have made us return and return." ]	{'aspect_term': [['bistro fare', 'positive'], ['chef', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Slightly above average wines start at $70+ with only one selection listed at $30+." ]	{'aspect_term': [['wines', 'negative']], 'aspect_category': [[None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "If you want good authentic Thai this place is not the place to go." ]	{'aspect_term': [['Thai', 'negative']], 'aspect_category': [[None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The food was just OK, I would never go back." ]	{'aspect_term': [['food', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The only positive was the wait staff, which was prompt, knowledgable, and likeable." ]	{'aspect_term': [['wait staff', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Interesting selection, good wines, service fine, fun decor." ]	{'aspect_term': [['wines', 'positive'], ['service', 'positive'], ['decor', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "I can't believe people complain about no cheese sticks?" ]	{'aspect_term': [['cheese sticks', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "very good breads as well." ]	{'aspect_term': [['breads', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The pizza is delicious - they use fresh mozzarella instead of the cheap, frozen, shredded cheese common to most pizzaria's." ]	{'aspect_term': [['pizza', 'positive'], ['fresh mozzarella', 'positive'], ['cheese', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The sushi seemed pretty fresh and was adequately proportioned." ]	{'aspect_term': [['sushi', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Get your food to go, find a bench, and kick back with a plate of dumplings." ]	{'aspect_term': [['food', 'neutral'], ['plate of dumplings', 'positive']], 'aspect_category': [[None, 'neutral'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "I have known about this secret for the last 13 years, Emilio(the Godfather) has continued to serve food and wine for the gods at mortal prices." ]	{'aspect_term': [['food', 'positive'], ['wine', 'positive'], ['prices', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The Thai ice tea was amazingly smooth and yummy!" ]	{'aspect_term': [['Thai ice tea', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were." ]	{'aspect_term': [['food', 'negative'], ['employees', 'negative'], ['containers for condiments', 'negative'], ['containers', 'neutral'], ['manager', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'neutral'], [None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "From the appetizers we ate, the dim sum and other variety of foods, it was impossible to criticize the food." ]	{'aspect_term': [['appetizers', 'positive'], ['dim sum', 'positive'], ['foods', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "My wife and I will usually only order one primi and one secondi and split them, as they tend to offer large portions." ]	{'aspect_term': [['primi', 'positive'], ['secondi', 'positive'], ['portions', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "We love the food, drinks, and atmosphere!" ]	{'aspect_term': [['food', 'positive'], ['drinks', 'positive'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "But they've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza (a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual)." ]	{'aspect_term': [['Godmother pizza (a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual)', 'positive'], ['specials', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "I do suggest to ask to be seated upstairs if you are looking to be a little cozy." ]	{'aspect_term': [['upstairs', 'positive']], 'aspect_category': [[None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "Great spot, whether looking for a couple of drinks or quiet dinner." ]	{'aspect_term': [['drinks', 'positive'], ['dinner', 'positive'], ['spot', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "They don't walk around with the trays of Dim Sum." ]	{'aspect_term': [['trays of Dim Sum', 'neutral']], 'aspect_category': [[None, 'neutral']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "What I didn't like was how the food came right after it was ordered." ]	{'aspect_term': [['food', 'negative']], 'aspect_category': [[None, 'negative']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The shrimp scampi was excellent and the antipasti were plentiful." ]	{'aspect_term': [['shrimp scampi', 'positive'], ['antipasti', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "You don't go to Mizu for excellent service, you go for the large amounts of food, the amiable atmosphere, and the hole-in-the-wall feeling of the place." ]	{'aspect_term': [['service', 'negative'], ['food', 'positive'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'negative'], [None, 'positive'], [None, 'positive']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation	semeval-2014	[ "The food is decent at best, and the ambience, well, it's a matter of opinion, some may consider it to be a sweet thing, I thought it was just annoying." ]	{'aspect_term': [['food', 'neutral'], ['ambience', 'conflict']], 'aspect_category': [[None, 'neutral'], [None, 'conflict']]}	none	Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The driver updates don't fix the issue, very frustrating." ]

[['driver updates', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "First, it does not have a push button to open the lid." ]

[['push button', 'negative'], ['lid', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I agree with the previous comment that ASUS TECH SUPPORT IS HORRIBLE WHICH IS A CON IN MY OPINION." ]

[['ASUS TECH SUPPORT', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The biggest problem is that the box had no instructions in it." ]

[['instructions', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Screen is awesome, battery life is good." ]

[['Screen', 'positive'], ['battery life', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It's not a wear-and-tear issue, not due to user carelessness and most importantly, they CAN'T guarantee the problem will be solved if it is sent for service and I have to accept the outcome." ]

[['service', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "We also use Paralles so we can run virtual machines of Windows XP Professional, Windows 7 Home Premium, Windows Server Enterprise 2003, and Windows Server 2008 Enterprise." ]

[['Paralles', 'neutral'], ['Windows XP Professional', 'neutral'], ['Windows 7 Home Premium', 'neutral'], ['Windows Server Enterprise 2003', 'neutral'], ['Windows Server 2008 Enterprise', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The first fell apart right after the 1-year-warranty." ]

[['1-year-warranty', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It seems they could have updated XP and done without creating Vista." ]

[['XP', 'positive'], ['Vista', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I wouldn't play a first-person shooter with this, mind, but if you wanted to run MS Office, email, chat, download a video, listen to music from a certain fruit-named music store, and were looking for a highly portable yet powerful netbook to do that all in, I'd highly recommend checking this out." ]

[['MS Office', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "and looks very sexyy:D really the mac book pro is the best laptop specially for students in college if you are not caring about price." ]

[['price', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The only thing I wish this had was the option to turn off the touchpad with a button like my big 16\" laptop does." ]

[['touchpad', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The only thing i can say is that the touch pad doesnt work like it should all the time." ]

[['touch pad', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "My husband uses it mostly for games, email and music." ]

[['games', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ " Until I bought the Dell, I thought you just looked for what you wanted (size, software, options, hardware) and purchase the best deal you could find." ]

[['size', 'neutral'], ['software', 'neutral'], ['hardware', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "After a little more than a year of owning my MacBook Pro, the monitor has completely died." ]

[['monitor', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "DO NOT BUY GATEWAY COMPUTERS THEY ARE JUNK AND THE WARRANTY COMPANY IS HORRIBLE." ]

[['WARRANTY COMPANY', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I could save ten essay papers and have hardly any memory left." ]

[['memory', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It even has a great webcam, and Skype works very well." ]

[['webcam', 'positive'], ['Skype', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The newer black keyboard took a little bit away from the previous gray one which looked really slick, but it is still a great notebook!" ]

[['black keyboard', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I picked it out because it was inexpensive ($400) and I thought it would be a good, easy to use first laptop." ]

[['use', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "wonderful features." ]

[['features', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Its ease of use and the top service from Apple- be it their phone assistance or bellying up to the genius bar- cannot be beat." ]

[['use', 'positive'], ['service', 'positive'], ['phone assistance', 'positive'], ['genius bar', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "my niece and nephew have played a few web games and it runs anything that doesn't require a dedicated video card." ]

[['video card', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "-4 RAM slots, 2 HDD Bays*, 16GB RAM support -No Wireless Issues, at least for me." ]

[['RAM slots', 'neutral'], ['HDD Bays', 'neutral'], ['16GB RAM support', 'neutral'], ['Wireless', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The computer itself was fast, ran smoothly, and had no problems." ]

[['ran', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "We carry the netbook around here and there, hence it's kinda of irritating when the LCD just \"slide\" downwards." ]

[['LCD', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "My Toshiba did not have sound on everything, just certain things." ]

[['sound', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Its pretty fast and does not have hiccups while I am using it for web browsing, uploading photos, watching movies (720p) on occasion and creating presentations." ]

[['web browsing', 'positive'], ['uploading photos', 'positive'], ['watching movies', 'positive'], ['creating presentations', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "While Apple's saving grace is the fact that they at least stand behind their products, and their support is great, it would be nice if their products were more reliable to justify the premium." ]

[['support', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It is absolutely horrible to use, despite all its so called advanced features." ]

[['features', 'negative'], ['use', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "After replacing the hard drive the battery stopped working (3 months of use) which was frustrating." ]

[['hard drive', 'neutral'], ['battery', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "This is a review of windows vista system." ]

[['windows vista system', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The brand is tarnished in my heart." ]

[['brand', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Boots up fast and runs great!" ]

[['Boots up', 'positive'], ['runs', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "After talking it over with the very knowledgeable sales associate, I chose the MacBook Pro over the white MacBook." ]

[['sales associate', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I love my Apple, it is quick and easy to use." ]

[['use', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Fully charged, the MacBook Pro can last about five hours unplugged." ]

[['charged', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I cannot be happier with the service or product." ]

[['service', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "the manufacturer's warranty only covers replacement/repair of parts." ]

[['warranty', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The best thing about this laptop is the price along with some of the newer features." ]

[['price', 'positive'], ['features', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Just keep in mind the graphics is not dedicated so loading the movie almost took a minute, but it ran fairly smooth for a non-dedicated graphics card." ]

[['graphics', 'conflict'], ['non-dedicated graphics card', 'conflict']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It's so much easier to navigate through the operating system, to find files, and it runs a lot faster!" ]

[['operating system', 'positive'], ['runs', 'positive'], ['navigate', 'positive'], ['find files', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The trackpad was easy to learn and navigate." ]

[['trackpad', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Games being the main issue." ]

[['Games', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "But to be honest, I don't use my computer for anything like graphics editing and complex data analysis and gaming." ]

[['gaming', 'neutral'], ['graphics editing', 'neutral'], ['complex data analysis', 'neutral']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "A month or so ago, the freaking motherboard just died." ]

[['motherboard', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "We upgraded the memory to four gigabytes in order to take advantage of the performace increase in speed." ]

[['memory', 'neutral'], ['speed', 'positive'], ['performace', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The keyboard is too slick." ]

[['keyboard', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Not even safe mode boots." ]

[['safe mode', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I have had another Mac, but it got slow due to an older operating system." ]

[['operating system', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I can barely use any usb devices because they will not stay connected properly." ]

[['usb devices', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The OS is also very user friendly, even for those that switch from a PC, with a little practice you can take full advantage of this OS!" ]

[['OS', 'positive'], ['OS', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The real stand out on this computer is the feel of the keyboard and it's speed." ]

[['keyboard', 'positive'], ['speed', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The technical service for dell is so 3rd world it might as well not even bother." ]

[['technical service for dell', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ ":-)If you buy this - don't go into it expecting 7 hrs of battery life, and you'll be perfectly satisfied." ]

[['battery life', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "Overall, this laptop is definitely a keeper with its simple yet stylish design and its array of fantastic colors to choose from." ]

[['design', 'positive'], ['colors', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "and the multiple page viewer ( allows you to press one button to see every separate page currently opened at the same time in one screen) are great for those who are working non stop or just shopping online." ]

[['multiple page viewer', 'positive']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "I was sorly disapointed to discover that HP (what I thought was a reputable company) wouldn't honor the warrenty when the fan blade fell apart." ]

[['warrenty', 'negative'], ['fan blade', 'negative']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "The mouse is a little bit different than what you're used to though- it has one big flat panel and one full bar (instead of two separate ones) to click with- but you get used to it quite quickly." ]

[['mouse', 'conflict']]

none

Task: Extracting aspect terms and their corresponding sentiment polarities. Input: A sentence. Output: A list of 2-tuples, where each tuple contains the extracted aspect category and their corresponding sentiment polarity. Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The first full charge of this battery got me only about 2 full hours." Output: [['battery', 'negative'], ['full charge', 'negative']]

generation

semeval-2014

[ "It is far more popular as a bar than as a restaurant, with only a few tables and the waiter being the bartender, but we greatly enjoyed the unobtrusive atmosphere." ]

{'aspect_term': [['bar', 'positive'], ['tables', 'negative'], ['waiter', 'neutral'], ['bartender', 'neutral'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'negative'], [None, 'neutral'], [None, 'neutral'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The food was very well prepared." ]

{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The food is great and authentic." ]

{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "They are tasty, but I suggest only eating one with meat because they tend not to mesh that well with the average American digestive system." ]

{'aspect_term': [['meat', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "I went there for lunch and it was not as good as I expected from the reviews I read." ]

{'aspect_term': [['lunch', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way." ]

{'aspect_term': [['menu', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Good food at the restaurant (a bit expensive, but great if you want to impress your date)." ]

{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The ambience was so fun, and the prices were great, on top of the fact that the food was really tasty." ]

{'aspect_term': [['ambience', 'positive'], ['prices', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Service was decent, but not as smooth as I would expect from a place with these prices and reputation." ]

{'aspect_term': [['Service', 'conflict'], ['prices', 'negative'], ['reputation', 'positive']], 'aspect_category': [[None, 'conflict'], [None, 'negative'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The food looked very appetizing and delicious since it came on a variety of fancy plates." ]

{'aspect_term': [['food', 'positive'], ['plates', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The waiter was attentive." ]

{'aspect_term': [['waiter', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The hot and sour soup was unbearably hot and tasted of only pepper and nothing else." ]

{'aspect_term': [['soup', 'negative'], ['pepper', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Decent wine at reasonable prices." ]

{'aspect_term': [['wine', 'positive'], ['prices', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "I'd call it an 'italian dinner'." ]

{'aspect_term': [['dinner', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The pizza was really good." ]

{'aspect_term': [['pizza', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "We've been following chef Lyle's food around New York for 15 years and while remaining classic, his innovations with bistro fare have made us return and return." ]

{'aspect_term': [['bistro fare', 'positive'], ['chef', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Slightly above average wines start at $70+ with only one selection listed at $30+." ]

{'aspect_term': [['wines', 'negative']], 'aspect_category': [[None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "If you want good authentic Thai this place is not the place to go." ]

{'aspect_term': [['Thai', 'negative']], 'aspect_category': [[None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The food was just OK, I would never go back." ]

{'aspect_term': [['food', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The only positive was the wait staff, which was prompt, knowledgable, and likeable." ]

{'aspect_term': [['wait staff', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Interesting selection, good wines, service fine, fun decor." ]

{'aspect_term': [['wines', 'positive'], ['service', 'positive'], ['decor', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "I can't believe people complain about no cheese sticks?" ]

{'aspect_term': [['cheese sticks', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "very good breads as well." ]

{'aspect_term': [['breads', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The pizza is delicious - they use fresh mozzarella instead of the cheap, frozen, shredded cheese common to most pizzaria's." ]

{'aspect_term': [['pizza', 'positive'], ['fresh mozzarella', 'positive'], ['cheese', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The sushi seemed pretty fresh and was adequately proportioned." ]

{'aspect_term': [['sushi', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Get your food to go, find a bench, and kick back with a plate of dumplings." ]

{'aspect_term': [['food', 'neutral'], ['plate of dumplings', 'positive']], 'aspect_category': [[None, 'neutral'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "I have known about this secret for the last 13 years, Emilio(the Godfather) has continued to serve food and wine for the gods at mortal prices." ]

{'aspect_term': [['food', 'positive'], ['wine', 'positive'], ['prices', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The Thai ice tea was amazingly smooth and yummy!" ]

{'aspect_term': [['Thai ice tea', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were." ]

{'aspect_term': [['food', 'negative'], ['employees', 'negative'], ['containers for condiments', 'negative'], ['containers', 'neutral'], ['manager', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'neutral'], [None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "From the appetizers we ate, the dim sum and other variety of foods, it was impossible to criticize the food." ]

{'aspect_term': [['appetizers', 'positive'], ['dim sum', 'positive'], ['foods', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "My wife and I will usually only order one primi and one secondi and split them, as they tend to offer large portions." ]

{'aspect_term': [['primi', 'positive'], ['secondi', 'positive'], ['portions', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "We love the food, drinks, and atmosphere!" ]

{'aspect_term': [['food', 'positive'], ['drinks', 'positive'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "But they've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza (a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual)." ]

{'aspect_term': [['Godmother pizza (a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual)', 'positive'], ['specials', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "I do suggest to ask to be seated upstairs if you are looking to be a little cozy." ]

{'aspect_term': [['upstairs', 'positive']], 'aspect_category': [[None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "Great spot, whether looking for a couple of drinks or quiet dinner." ]

{'aspect_term': [['drinks', 'positive'], ['dinner', 'positive'], ['spot', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "They don't walk around with the trays of Dim Sum." ]

{'aspect_term': [['trays of Dim Sum', 'neutral']], 'aspect_category': [[None, 'neutral']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "What I didn't like was how the food came right after it was ordered." ]

{'aspect_term': [['food', 'negative']], 'aspect_category': [[None, 'negative']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The shrimp scampi was excellent and the antipasti were plentiful." ]

{'aspect_term': [['shrimp scampi', 'positive'], ['antipasti', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "You don't go to Mizu for excellent service, you go for the large amounts of food, the amiable atmosphere, and the hole-in-the-wall feeling of the place." ]

{'aspect_term': [['service', 'negative'], ['food', 'positive'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'negative'], [None, 'positive'], [None, 'positive']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}

generation

semeval-2014

[ "The food is decent at best, and the ambience, well, it's a matter of opinion, some may consider it to be a sweet thing, I thought it was just annoying." ]

{'aspect_term': [['food', 'neutral'], ['ambience', 'conflict']], 'aspect_category': [[None, 'neutral'], [None, 'conflict']]}

none

Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}