A Benchmark Dataset for Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Applications
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset is intended to support sentiment analysis of users' reviews on COVID-19 contact tracing mobile applications. The dataset is composed of a total of 34,53 manually annotated users' reviews from 46 different applications used in different parts of the world. These reviews are categorized into 4 categories including positive, negative, neutral, and technical issues. The positive class is composed of 15,587 reviews, 8,178 reviews are in the negative class while the neutral and technical issues classes are composed of 1,271 and 9,496, respectively. The data is intended to cover three different tasks. The details of each task are provided in the paper. Moreover, in order to facilitate the potential users of the dataset, we are providing the task-wise distribution of reviews for each task in a separate folder. Each folder provide' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["COVID-19, Sentiment Analysis, NLP, BERT, Contact Tracing Applications, Dataset, COVID_19 Dataset, Apps Reviews"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset is intended to support sentiment analysis of users' reviews on COVID-19 contact tracing mobile applications. The dataset is composed of a total of 34,53 manually annotated users' reviews from 46 different applications used in different parts of the world. These reviews are categorized into 4 categories including positive, negative, neutral, and technical issues. The positive class is composed of 15,587 reviews, 8,178 reviews are in the negative class while the neutral and technical issues classes are composed of 1,271 and 9,496, respectively. The data is intended to cover three different tasks. The details of each task are provided in the paper. Moreover, in order to facilitate the potential users of the dataset, we are providing the task-wise distribution of reviews for each task in a separate folder. Each folder provide' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset is intended to support sentiment analysis of users' reviews on COVID-19 contact tracing mobile applications. The dataset is composed of a total of 34,53 manually annotated users' reviews from 46 different applications used in different parts of the world. These reviews are categorized into 4 categories including positive, negative, neutral, and technical issues. The positive class is composed of 15,587 reviews, 8,178 reviews are in the negative class while the neutral and technical issues classes are composed of 1,271 and 9,496, respectively. The data is intended to cover three different tasks. The details of each task are provided in the paper. Moreover, in order to facilitate the potential users of the dataset, we are providing the task-wise distribution of reviews for each task in a separate folder. Each folder provide
COVID-19, Sentiment Analysis, NLP, BERT, Contact Tracing Applications, Dataset, COVID_19 Dataset, Apps Reviews