Title: Perplexed Bayes classifier-based secure and intelligent approach for aspect level sentiment analysis

Authors: Sumit Kumar Yadav; Devendra K. Tayal; Shiv Naresh Shivhare

Addresses: Computer Science Department, University School of Engineering and Technology, GGSIPU Delhi, India ' Department of Computer Science, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi-06, India ' CSE Department, Galgotias University, Greater Noida, Uttar Pradesh, India

Abstract: In this work, we are using machine learning methods to classify a review document. We are using two machine learning methods - Naive Bayes classifier and perplexed Bayes classifier. First we will briefly introduce the Naive Bayes classifier, its shortcomings and perplexed Bayes classifier. Further, we will be training the classifiers using a small training set and will use a test set with reviews having dependency among its features. We will then show that how Naive Bayes classifier fails to classify such reviews and will be showing that perplexed Bayes classifier can be used to classify the given test set, having dependency among its features.

Keywords: sentiment-analysis; machine-learning techniques; Naïve Bayes; perplexed Bayes; aspect level sentiment analysis.

DOI: 10.1504/IJAIP.2019.099941

International Journal of Advanced Intelligence Paradigms, 2019 Vol.13 No.1/2, pp.15 - 31

Received: 24 Aug 2016
Accepted: 01 Oct 2016
Published online: 29 May 2019 *

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