Abstract

This paper describes our submission for SemEval2013 Task 2: Sentiment Analysis in Twitter. For the limited data condition we use a lexicon-based model. The model uses an affective lexicon automatically generated from a very large corpus of raw web data. Statistics are calculated over the word and bigram affective ratings and used as features of a Naive Bayes tree model. For the unconstrained data scenario we combine the lexicon-based model with a classifier built on maximum entropy language models and trained on a large external dataset. The two models are fused at the posterior level to produce a final output. The approach proved successful, reaching rankings of 9th and 4th in the twitter sentiment analysis constrained and unconstrained scenario respectively, despite using only lexical features.

Date

2013

Authors

Nikolaos Malandrakis, Abe Kazemzadeh, Alexandros Potamianos, Shrikanth Narayanan

Conference

Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

Pages

438-442