Abstract
We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object by using a continuous measure called sentiment polarity score (SP score) rather than binary polarity (good or bad). An SP score gives a concise summary of a review, and provides more information than binary classification. The difficulty of this task lies in the quantification of polarity. In this paper we use support vector regression (SVR) to tackle this problem. Experiments on book reviews using five-point scales show that SVR outperforms a multi-class classification method using support vector machines, and the results are close to human performance. We also examine the effect of sentence subjectivity detection using a Naive Bayes classifier, and show that this improves the robustness of the classifier.
