Paper: A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

ACL ID P10-1141
Title A Study of Information Retrieval Weighting Schemes for Sentiment Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

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 vari- ants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when us- ing a sublinear function for term frequency weights and document frequency smooth- ing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.