Target Based Review Classification for Fine-grained Sentiment Analysis

Target based sentiment classification is able to provide more fine grained sentiment analysis. In this paper, we propose a similarity based approach for this problem. Firstly, a new measure of PMI-TFIDF by combining PMI (Pointwise mutual information) and TF-IDF (term frequency-inverse document frequency) is proposed to measure the association of words for extending related features for a given target. Then Polynomial Kernel (PK) method is applied to get the similarities between a review and the related features of different targets. The sentiment orientation of a review is determined by comparing their similarities with the target based opinion words. The comparisons between PMI and PMI-TFIDF showed that the extracted features that measured by PMI-TFIDF have closer association with the targets than the extracted features measured by PMI. And the association values measured by PMI-TFIDF showed better distinction between different features. The experiments also demonstrated the effectiveness and validation of the proposed approach on target based review classification, opinion words extraction, and target based sentiment classification.