Paper: REACTION: A naive machine learning approach for sentiment classification

ACL ID S13-2081
Title REACTION: A naive machine learning approach for sentiment classification
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

We evaluate a naive machine learning ap- proach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a classifier based on the Random Forests al- gorithm to determine whether a tweet ex- presses overall positive, negative or neu- tral sentiment. The classifier was trained only with the provided dataset and uses as main features word vectors and lexicon word counts. Our average F-score for all three classes on the Twitter evaluation dataset was 51.55%. The average F-score of both positive and negative classes was 45.01%. For the optional SMS evaluation dataset our overall average F-score was 58.82%. The average between positive and negative F- scores was 50.11%.