Paper: [LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter

ACL ID S13-2069
Title [LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper presents the contribution of our team at task 2 of SemEval 2013: Sentiment Analysis in Twitter. We submitted a con- strained run for each of the two subtasks. In the Contextual Polarity Disambiguation subtask, we use a sentiment lexicon approach combined with polarity shift detection and tree kernel based classifiers. In the Message Polarity Clas- sification subtask, we focus on the influence of domain information on sentiment classifica- tion.