SemEval-2017 Task 4: Sentiment Analysis in Twitter
SemEval-2017 Task 4: Sentiment Analysis in Twitter
This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii)~we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.
sentiment analysis, Twitter, classification, quantification, ranking, English, Arabic
- University of Chicago United States
- King’s University United States
- Qatar Computing Research Institute Qatar
- Hamad bin Khalifa University Qatar
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, I.2.7, 68T50, Computation and Language (cs.CL), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, I.2.7, 68T50, Computation and Language (cs.CL), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)
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