Paper: Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media

ACL ID S13-2079
Title Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

We present a supervised sentiment detection system that classifies the polarity of subjec- tive phrases as positive, negative, or neutral. It is tailored towards online genres, specifically Twitter, through the inclusion of dictionaries developed to capture vocabulary used in on- line conversations (e.g., slang and emoticons) as well as stylistic features common to social media. We show how to incorporate these new features within a state of the art system and evaluate it on subtask A in SemEval-2013 Task 2: Sentiment Analysis in Twitter.