Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Capturing user sentiments for online Indian movie reviews

Capturing user sentiments for online Indian movie reviews 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.Design/methodology/approachIn 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).FindingsThe 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.Originality/valueThis is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Electronic Library Emerald Publishing

Capturing user sentiments for online Indian movie reviews

Loading next page...
 
/lp/emerald-publishing/capturing-user-sentiments-for-online-indian-movie-reviews-VTJzlgpnI6
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0264-0473
DOI
10.1108/el-04-2017-0075
Publisher site
See Article on Publisher Site

Abstract

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.Design/methodology/approachIn 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).FindingsThe 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.Originality/valueThis is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

Journal

The Electronic LibraryEmerald Publishing

Published: Oct 29, 2018

Keywords: Opinion mining; Indian movie reviews; Machine learning classifiers; User sentiment analysis

References