Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. 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, two methods are introduced for feature extraction. The recommended methods consist of four main stages. At the first stage, opinion-mining lexicon for Persian is created. This lexicon is used to determine the orientation of users’ reviews. The second one is the preprocessing stage including unification of writing, tokenization, creating parts-of-speech tagging and syntactic dependency parsing for documents. The third stage involves the extraction of features using two methods including frequency-based feature extraction and association rule based feature extraction. In the fourth stage, the features and polarities of the word reviews extracted in the previous stage are modified and the final features' 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 two methods were compared.