Improving Sentiment Polarity Detection Through Target Identification

Abstract

In an opinionated long review, there may be several targets described by different potential terms. Traditional review-level techniques for Persian sentiment analysis addressed the problem using a one-method-fits-all solution in which the overall polarity of a review is calculated using all its opinionated words without considering their target. In this article, a new method is proposed, which first decomposes a long review into its constituent sentences and then detects the main target of each sentence. In the next step, five policies, including most occurring first (MOF), most general first (MGF), most specific first (MSF), first occurring first (FOF), and last occurring first (LOF), are proposed to come up with the main target of the review. Finally, using the part-of-speech (POS) tags, potential terms in the sentences are specified and a comprehensive sentiment lexicon is employed to compute the polarity of the sentences. In order to evaluate the proposed method, three data sets of user reviews about different topics, including digital equipment, hotels, and movies, are created as no previous study addressed the problem of target identification in the Persian language. The results of comparing the proposed method with a state-of-the-art lexicon-based method show that specifying the main targets of reviews can improve the performance of the systems about 17% and 12% in terms of accuracy and F1-measure. Moreover, the proposed method using the MGF policy achieves the best performance in finding the main target of reviews, while for finding the ultimate polarity of reviews, the MOF outperforms other policies.