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

TítuloCitius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets
AutoresPablo Gamallo, Marcos Garcia
TipoPoster para congreso
Fonte 8th International Workshop on Semantic Evaluation (SemEval 2014),, Dublin (Ireland), pp. 171-175 , 2014.
ISBN978-1-941643-24-2
AbstractThis 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.
Palabras chaveSentiment Analysis, Microblogging, Naive Bayes Classification