Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to
sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing …

Abstract Sentiment Analysis in Twitter has been considered as a vital task for a decade from
various academic and commercial perspectives. Several works have been performed on …

This paper presents the first shared task on irony detection: given a tweet, automatic natural
language processing systems should determine whether the tweet is ironic (Task A) and …

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis
(ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task …

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 …

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats,
posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in …

We present the first Africentric SemEval Shared task, Sentiment Analysis for African
Languages (AfriSenti-SemEval)-The dataset is available at https://github. com/afrisenti …

In the last decade, the need to detect automatically irony to correctly recognize the sentiment
and hate speech involved in online texts increased the investigation on humorous figures of …

A large community of research has been developed in recent years to analyze social media
and social networks, with the aim of understanding, discovering insights, and exploiting the …

What are the limits of automated Twitter sentiment classification? We analyze a large set of
manually labeled tweets in different languages, use them as training data, and construct …