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In the recent decade, with the enormous growth of digital content in internetand databases, sentiment analysis has received more and more attention betweeninformation retrieval and natural language processing researchers. Sentimentanalysis aims to use automated tools to detect subjective information fromreviews. One of the main challenges in sentiment analysis is feature selection.Feature selection is widely used as the first stage of analysis andclassification tasks to reduce the dimension of problem, and improve speed bythe elimination of irrelevant and redundant features. Up to now as there arefew researches conducted on feature selection in sentiment analysis, there arevery rare works for Persian sentiment analysis. This paper considers theproblem of sentiment classification using different feature selection methodsfor online customer reviews in Persian language. Three of the challenges ofPersian text are using of a wide variety of declensional suffixes, differentword spacing and many informal or colloquial words. In this paper we studythese challenges by proposing a model for sentiment classification of Persianreview documents. The proposed model is based on lemmatization and featureselection and is employed Naive Bayes algorithm for classification. We evaluatethe performance of the model on a manually gathered collection of cellphonereviews, where the results show the effectiveness of the proposed approaches.

Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method