Name: SemEval-2017 Task 4
Description:  a large accessible benchmark dataset containing over 70,000 tweets
  across two languages
Provider: Multiple institutions
Paper Title: SemEval-2017 Task 4: Sentiment Analysis in Twitter
Tasks: sentiment analysis
Venue Name: International Workshop on Semantic Evaluation
Authors: Sara Rosenthal,N. Farra,Preslav Nakov
Abstract: 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.