SemEval-2016 Task 4: Sentiment Analysis in Twitter

TL;DR

This paper presents the fourth annual SemEval Twitter sentiment analysis task, introducing new subtasks including ordinal sentiment classification and quantification, attracting 43 participating teams.

Contribution

It introduces new subtasks for sentiment analysis in Twitter, including ordinal classification and quantification, expanding the scope of previous editions.

Findings

High participation with 43 teams.

Introduction of ordinal sentiment classification.

Focus on quantification of sentiment prevalence.

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.