Title
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method.
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.
Year
Venue
Keywords
2014
International journal of artificial intelligence
feature selection,natural language processing,naive bayes algorithm,sentiment analysis
Field
DocType
Volume
Lemmatisation,Naive Bayes classifier,Feature selection,Computer science,Persian,Sentiment analysis,Customer reviews,Artificial intelligence,Natural language processing,Digital content,The Internet
Journal
abs/1412.8079
Issue
ISSN
Citations 
2
International Journal of Artificial Intelligence 12.2 (2014): 115-129
1
PageRank 
References 
Authors
0.36
20
2
Name
Order
Citations
PageRank
Ayoub Bagheri1623.48
Mohamad Saraee210.36