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

Abstract

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