IIIT-H at SemEval 2015: Twitter Sentiment Analysis -- The Good, the Bad and the Neutral!

aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and c…

SemEval-2017 Task 4: Sentiment Analysis in Twitter

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 identifying the overall sentiment of the twe…

IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text

In this paper, we present the results that the team IIITG-ADBU (codalab username {`}abaruah{'}) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task r…

ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter

This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sen…