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    Capturing user sentiments for online Indian movie reviews.

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    Authors
    Trivedi, Shrawan Kumar
    Dey, Shubhamoy
    Kumar, Anil
    Affiliation
    Indian Institute of Management Sirmaur, Sirmaur, India
    Indian Institute of Management Indore, Indore, India
    University of Derby
    Issue Date
    2018-08-06
    
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    Abstract
    Sentiment analysis and opinion mining are emerging areas of research for analysing 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.
    Citation
    Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, (2018) ‘Capturing user sentiments for online Indian movie reviews: A comparative analysis of different machine-learning models’, The Electronic Library, 36 (4), pp.677-695, DOI: 10.1108/EL-04-2017-0075
    Publisher
    Emerald Insight
    Journal
    The Electronic Library
    URI
    http://hdl.handle.net/10545/623307
    DOI
    10.1108/EL-04-2017-0075
    Additional Links
    https://www.emeraldinsight.com/doi/10.1108/EL-04-2017-0075
    Type
    Article
    Language
    en
    ISSN
    0264-0473
    ae974a485f413a2113503eed53cd6c53
    10.1108/EL-04-2017-0075
    Scopus Count
    Collections
    Centre for Supply Chain Improvement

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