Perplexed Bayes classifier-based secure and intelligent approach for aspect level sentiment analysis. (17th May 2019)

• Record Type: Journal Article
• Authors: Yadav, Sumit Kumar; Tayal, Devendra K.; Shivhare, Shiv Naresh
• Is Part Of: International journal of advanced intelligence paradigms. Volume 13:Number 1/2(2019)
• Page Start: 15
• Page End: 31
• Publication Date: 2019-05-17
• Subjects: sentiment-analysis -- machine-learning techniques -- Naïve Bayes -- perplexed Bayes -- aspect level sentiment analysis; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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.