Paper: RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages

ACL ID S14-2086
Title RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the enhancements made to our GU-MLT-LT system (G?unther and Furrer, 2013) for the SemEval-2014 re-run of the SemEval-2013 shared task on sentiment analysis in Twitter. The changes include the usage of a Twitter-specific to- kenizer, additional features and sentiment lexica, feature weighting and random sub- space learning. The improvements result in an increase of 4.18 F-measure points on this year?s Twitter test set, ranking 3rd.