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Capturing user sentiments for online Indian movie reviews: A comparative analysis of different machine-learning models

Shrawan Kumar Trivedi (Department of IT and Systems, Indian Institute of Management Sirmaur , Sirmaur, India)
Shubhamoy Dey (Department of Information Systems, Indian Institute of Management Indore , Indore, India)
Anil Kumar (Department of Decision Science, BML Munjal University , Gurgaon, India)

The Electronic Library

ISSN: 0264-0473

Article publication date: 25 October 2018

Issue publication date: 29 October 2018




Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers.


In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).


The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM.


This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.



Trivedi, S.K., Dey, S. and Kumar, A. (2018), "Capturing user sentiments for online Indian movie reviews: A comparative analysis of different machine-learning models", The Electronic Library, Vol. 36 No. 4, pp. 677-695.



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