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Nakov P., Ritter A., Rosenthal S., Sebastiani F., Stoyanov V. SemEval-2016 Task 4: Sentiment analysis in Twitter. In: SemEval 2016 - 10th International Workshop on Semantic Evaluation (San Diego, US, 16-17 June 2016). Proceedings, pp. 1 - 18. Association for Computational Linguistics, 2016.
 
 
Abstract
(English)
This paper discusses the fourth year of the "Sentiment Analysis in Twitter Task". SemEval-2016 Task 4 comprises five sub- tasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic "sentiment classification in Twitter" task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.
URL: http://https://aclweb.org/anthology/S/S16/S16-1001.pdf
Subject Sentiment classification
I.2.6 ARTIFICIAL INTELLIGENCE. Learning


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