Paper: SZTE-NLP: Sentiment Detection on Twitter Messages

ACL ID S13-2092
Title SZTE-NLP: Sentiment Detection on Twitter Messages
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper we introduce our contribution to the SemEval-2013 Task 2 on ?Sentiment Analysis in Twitter?. We participated in ?task B?, where the objective was to build mod- els which classify tweets into three classes (positive, negative or neutral) by their con- tents. To solve this problem we basically fol- lowed the supervised learning approach and proposed several domain (i.e. microblog) spe- cific improvements including text preprocess- ing and feature engineering. Beyond the su- pervised setting we also introduce some early results employing a huge, automatically anno- tated tweet dataset.