Paper: sielers : Feature Analysis and Polarity Classification of Expressions from Twitter and SMS Data

ACL ID S13-2087
Title sielers : Feature Analysis and Polarity Classification of Expressions from Twitter and SMS Data
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into ac- count the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were per- formed on the features to find out whether they were more suited for subjectivity or po- larity Classification. We tested our model for sentiment polarity classification on Twitter as well as SMS chat expressions, analyzed their F-measure scores and drew some interesting conclusions from them.