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Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

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Abstract

With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.

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Authors and Affiliations

  1. Research Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India

    Asha S Manek

  2. Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, 560 001, India

    P Deepa Shenoy & Venugopal K R

  3. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India

    M Chandra Mohan

Authors
  1. Asha S Manek
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  2. P Deepa Shenoy
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  3. M Chandra Mohan
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  4. Venugopal K R
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Correspondence to Asha S Manek.

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Manek, A.S., Shenoy, P.D., Mohan, M.C. et al. Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web 20, 135–154 (2017). https://doi.org/10.1007/s11280-015-0381-x

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  • DOI: https://doi.org/10.1007/s11280-015-0381-x

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