bwbaugh : Hierarchical sentiment analysis with partial self-training

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 hierarchical classifier. Using additional unlabeled data that is believed to contain sentiment, we allow the polarity classifier to continue learning 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