Paper: KLUE: Simple and robust methods for polarity classification

ACL ID S13-2065
Title KLUE: Simple and robust methods for polarity classification
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper describes our approach to the SemEval-2013 task on ?Sentiment Analysis in Twitter?. We use simple bag-of-words mod- els, a freely available sentiment dictionary auto- matically extended with distributionally similar terms, as well as lists of emoticons and inter- net slang abbreviations in conjunction with fast and robust machine learning algorithms. The resulting system is resource-lean, making it rel- atively independent of a specific language. De- spite its simplicity, the system achieves compet- itive accuracies of 0.70?0.72 in detecting the sentiment of text messages. We also apply our approach to the task of detecting the context- dependent sentiment of individual words and phrases within a message.