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Proceedings Paper

A SVM-based method for sentiment analysis in Persian language
Author(s): Mohammad Sadegh Hajmohammadi; Roliana Ibrahim
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Paper Abstract

Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

Paper Details

Date Published: 20 March 2013
PDF: 5 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876838 (20 March 2013); doi: 10.1117/12.2010940
Show Author Affiliations
Mohammad Sadegh Hajmohammadi, Islamic Azad Univ. (Iran, Islamic Republic of)
Roliana Ibrahim, Univ. Teknologi Malaysia (Malaysia)

Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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