Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media
Sara Rosenthal, Kathy McKeown, Apoorv Agarwal · 2014
We present two supervised sentiment de-tection systems which were used to com-pete in SemEval-2014 Task 9: Senti-ment Analysis in Twitter. The first sys-tem (Rosenthal and McKeown, 2013) clas-sifies the polarity of subjective phrases as positive, negative, or neutral. It is tai-lored towards online genres, specifically Twitter, through the inclusion of dictionar-ies developed to capture vocabulary used in online conversations (e.g., slang and emoticons) as well as stylistic features common to social media. The second sys-tem (Agarwal et al., 2011) classifies entire tweets as positive, negative, or neutral. It too includes dictionaries and stylistic fea-tures developed for social media, several of which are distinctive from those in the first system. We use both systems to par-