Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.
Paltoglou and Thelwall REF found that variants of the classic TF-IDF scheme adapted to sentiment analysis provided significant increases in accuracy.
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis
ACL
Computer Science