SemEval-2016 Task 4: Sentiment Analysis in Twitter
Abstract
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.
Cite
@article{arxiv.1912.01973,
title = {SemEval-2016 Task 4: Sentiment Analysis in Twitter},
author = {Preslav Nakov and Alan Ritter and Sara Rosenthal and Fabrizio Sebastiani and Veselin Stoyanov},
journal= {arXiv preprint arXiv:1912.01973},
year = {2021}
}
Comments
Sentiment analysis, sentiment towards a topic, quantification, microblog sentiment analysis; Twitter opinion mining. arXiv admin note: text overlap with arXiv:1912.00741