Feature Extraction and Lexicon Expanded in Opinion Mining through Persian Reviews

Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which plays an important role in making major decisions in such areas. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. Opinion mining at the feature level is taken into consideration more than the other two levels due to orientation analysis of different aspects of an area. In this paper, one method is introduced for a feature extraction. The recommended method consists of four main stages. First, opinion-mining lexicon for Persian is created. This lexicon is used to determine the orientation of users’ reviews. Second, the preprocessing stage includes unification of writing, tokenization, creating parts-of-speech tagging and syntactic dependency parsing for documents. Third, the extraction of features uses the method including dependency grammar based feature extraction. Fourth, the features and polarities of the word reviews extracted in the previous stage are modified and the final feature's polarity is determined. To assess the suggested techniques, a set of user reviews in both scopes of university and cell phone areas were collected and the results of the method were compared with frequency-based feature extraction method.