Which Twitter sentiment treebank is used?
They built their own
This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).
Sentiment detection is the process of determining whether a text has a positive or negative attitude toward a given entity (topic) or in general. Detecting sentiment on Twitter—a social network where users interact via short 140-character messages, exchanging information and opinions—is becoming ubiquitous. Sentiment in Twitter messages (tweets) can capture the popularity level of political figures, ideas, brands, products and people. Tweets and other social media texts are challenging to analyze as they are inherently different; use of slang, mis-spelling, sarcasm, emojis and co-mentioning of other messages pose unique difficulties. Combined with the vast amount of Twitter data (mostly public), these make sentiment detection on Twitter a focal point for data science research. SemEval is a yearly event in which teams compete in natural language processing tasks. Task 4 is concerned with sentiment analysis in Twitter; it contains five sub-tasks which include classification of tweets according to 2, 3 or 5 labels and quantification of sentiment distribution regarding topics mentioned in tweets; for a complete description of task 4 see BIBREF0 . This paper describes our system and participation in all sub-tasks of SemEval 2017 task 4. Our system consists of two parts: a recurrent neural network trained on a private Twitter dataset, followed by a task-specific combination of model stacking and logistic regression classifiers. The paper is organized as follows: section SECREF2 describes the training of RNN models, data being used and model selection; section SECREF3 describes the extraction of semantic features; section SECREF4 describes the task-specific workflows and scores. We review and summarize in section SECREF5 . Finally, section SECREF6 describes our future plans, mainly the development of an LSTM algorithm.