  In the recent decade, with the enormous growth of digital content in internet
and databases, sentiment analysis has received more and more attention between
information retrieval and natural language processing researchers. Sentiment
analysis aims to use automated tools to detect subjective information from
reviews. One of the main challenges in sentiment analysis is feature selection.
Feature selection is widely used as the first stage of analysis and
classification tasks to reduce the dimension of problem, and improve speed by
the elimination of irrelevant and redundant features. Up to now as there are
few researches conducted on feature selection in sentiment analysis, there are
very rare works for Persian sentiment analysis. This paper considers the
problem of sentiment classification using different feature selection methods
for online customer reviews in Persian language. Three of the challenges of
Persian text are using of a wide variety of declensional suffixes, different
word spacing and many informal or colloquial words. In this paper we study
these challenges by proposing a model for sentiment classification of Persian
review documents. The proposed model is based on lemmatization and feature
selection and is employed Naive Bayes algorithm for classification. We evaluate
the performance of the model on a manually gathered collection of cellphone
reviews, where the results show the effectiveness of the proposed approaches.
