Paper: bwbaugh : Hierarchical sentiment analysis with partial self-training

ACL ID S13-2090
Title bwbaugh : Hierarchical sentiment analysis with partial self-training
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

Using labeled Twitter training data from SemEval-2013, we train both a subjectivity classifier and a polarity classifier separately, and then combine the two into a single hier- archical classifier. Using additional unlabeled data that is believed to contain sentiment, we allow the polarity classifier to continue learn- ing using self-training. The resulting system is capable of classifying a document as neutral, positive, or negative with an overall accuracy of 61.2% using our hierarchical Naive Bayes classifier.1