LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set
Cynthia Van Hee, Marjan Van de Kauter, Orphée De Clercq, Els Lefever, Véronique Hoste · 2014
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 experiments with a variety of lexical and syntactic features show that our systems benefit from rich feature 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.