Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media
Sara Rosenthal, Kathy McKeown · 2013
We present a supervised sentiment detection system that classifies the polarity of subjective 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 online 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. 1