Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media

Abstract

We present two supervised sentiment detection systems which were used to compete in SemEval-2014 Task 9: Sentiment Analysis in Twitter. The first system (Rosenthal and McKeown, 2013) 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. The second system (Agarwal et al., 2011) classifies entire tweets as positive, negative, or neutral. It too includes dictionaries and stylistic features developed for social media, several of which are distinctive from those in the first system. We use both systems to participate in Subtasks A and B of SemEval-2014 Task 9: Sentiment Analysis in Twitter. We participated for the first time in Subtask B: Message-Level Sentiment Detection by combining the two systems to achieve improved results compared to either system alone.