Feature Weighting Strategies in Sentiment Analysis

In this paper we propose an adaptation of the KullbackLeibler divergence score for the task of sentiment and opinion classification on a sentence level. We propose to use the obtained score with the SVM model using different thresholds for pruning the feature set. We argue that the pruning of the feature set for the task of sentiment analysis (SA) may be detrimental to classifiers performance on short text. As an alternative approach, we consider a simple additive scheme that takes into account all of the features. Accuracy rates over 10 fold cross-validation indicate that the latter approach outperforms the SVM classification scheme.