Paper: Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets

ACL ID S14-2026
Title Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This article describes a strategy based on a naive-bayes classifier for detecting the po- larity of English tweets. The experiments have shown that the best performance is achieved by using a binary classifier be- tween just two sharp polarity categories: positive and negative. In addition, in or- der to detect tweets with and without po- larity, the system makes use of a very basic rule that searchs for polarity words within the analysed tweets/texts. When the clas- sifier is provided with a polarity lexicon and multiwords it achieves 63% F-score.