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Capturing user sentiments for online Indian movie reviews: A comparative analysis of different machine-learning models
Trivedi S K, , Kumar A
Published in Emerald Publishing
2018
Volume: 36
   
Issue: 4
Pages: 677 - 695
Abstract

Purpose
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/approach
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).

Findings
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.

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

About the journal
JournalData powered by TypesetThe Electronic Library
PublisherData powered by TypesetEmerald Publishing
ISSN0264-0473
Open AccessNo