Paper: LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set

ACL ID S14-2070
Title LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and polarity disambiguation of particular text spans within a message (subtask A). Our experi- ments with a variety of lexical and syntactic fea- tures show that our systems benefit from rich fea- ture sets for sentiment analysis on user-generated content. Our systems ranked ninth among 27 and sixteenth among 50 submissions for task A and B respectively.