Persian sentiment analyzer : a framework based on a novel
feature selection method

Bagheri, A; Saraee, MH

Abstract

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.

Citation

feature selection method. International Journal of Artificial Intelligence, 12(2), 115-129

• Journal Article Type: Article
• Publication Date: Oct 1, 2014
• Deposit Date: Jul 24, 2017
• Journal: International Journal of Artificial Intelligence
• Print ISSN: 2356-5888
• Publisher: N&N Global Technology
• Volume: 12
• Issue: 2
• Pages: 115-129
• Publisher URL: http://www.ceser.in/ceserp/index.php/ijai/article/view/3322
• Related Public URLs: http://www.ceser.in/ceserp/index.php/ijai