arXiv 1912.019732019-12-03cs.CLcs.IR

SemEval-2016 Task 4: Sentiment Analysis in Twitter

Qatar Computing Research Institute, Hamad bin Khalifa University（卡塔尔计算研究所，哈马德·本·哈利法大学）；The Ohio State University（俄亥俄州立大学）；IBM Watson Health Research（IBM沃森健康研究中心）；Consiglio Nazionale delle Ricerche（意大利国家研究委员会）；Johns Hopkins University（约翰斯·霍普金斯大学）

Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov

备注：: Sentiment analysis, sentiment towards a topic, quantification, microblog sentiment analysis; Twitter opinion mining. arXiv admin note: text overlap with arXiv:1912.00741
发表信息：: Final version published in the Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, US, 2016, pp. 1-18

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英文摘要：

This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, 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.

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