Paper: UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data

ACL ID S14-2126
Title UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

We present a sentiment classification sys- tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit- ter. Our system expands tokens in a tweet with semantically similar expressions us- ing a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists repre- senting positive and negative sentiment. This approach helps to assess the polarity of tweets that do not directly contain po- larity cues. Moreover, we incorporate syn- tactic, lexical and surface sentiment fea- tures. On the message level, our system achieved the 8th place in terms of macro- averaged F-score among 50 systems, with particularly good performance on the Life- Journal corpus (F 1 =71.92) and the Twitter sarcasm (F 1 =54.59) dataset. On the ex...