Paper: CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features

ACL ID S14-2025
Title CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper presents the approach of the CISUC-KIS team to the SemEval 2014 task on Sentiment Analysis in Twitter, more precisely subtask B - Message Polar- ity Classification. We followed a machine learning approach where a SVM classifier was trained from a large and diverse set of features that included lexical, syntac- tic, sentiment and semantic-based aspects. This led to very interesting results which, in different datasets, put us always in the top-7 scores, including second position in the LiveJournal2014 dataset.