Design, Improvement, Development, and Performance Analysis of a Collection of Model Developed From Naïve Bayes and Maximum Entropy Opinion Mining Classifiers for Movie Reviews

  • Authors

    • Dr Lokesh A
    • Mr Yerriswamy T
    • Mr Venkatagiri J
    • Mr Pradeep. M
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.17908
  • Reviews, measure, Sentiment analysis, Naïve Bayes, Maximum Entropy, bigrams, ensemble model, hybrid algorithm

  • Abstract

    The internet is a basic platform for people from every one walks of life to interconnect and convey opinions on the topic of their choice. Almost every website asks for comments, suggestions and reviews. Exploring opinion and determining a person’s views is itself a large subject in computer science, known as Opinion Mining, also called Sentiment Analysis  There are different sentiment classifiers, the most admired of which are the Naïve Bayes classifier, maintain Vector Machines (SVM), Maximum Entropy classifier, to name a few. In this paper, here we are analyzing the efficient performance of the Naïve Bayes also about the Maximum Entropy classifiers. Here we analyze and examine how bigrams perform better than unigrams in sentiment analysis. We further propose a serialized ensemble model of the two as a hybrid algorithm and analyze its performance as well.  

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  • How to Cite

    Lokesh A, D., Yerriswamy T, M., Venkatagiri J, M., & Pradeep. M, M. (2018). Design, Improvement, Development, and Performance Analysis of a Collection of Model Developed From Naïve Bayes and Maximum Entropy Opinion Mining Classifiers for Movie Reviews. International Journal of Engineering & Technology, 7(2.33), 1064-1067. https://doi.org/10.14419/ijet.v7i2.33.17908

    Received date: 2018-08-19

    Accepted date: 2018-08-19

    Published date: 2018-06-08