Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naive Bayes

TL;DR

This paper presents a machine learning approach for sentiment polarity detection in Bengali book reviews, demonstrating that multinomial Naive Bayes with unigram features achieves 84% accuracy, outperforming other methods.

Contribution

The study introduces a Bengali review corpus and compares multiple classifiers, highlighting the effectiveness of multinomial Naive Bayes with unigram features for sentiment analysis.

Findings

Multinomial Naive Bayes with unigram features achieves 84% accuracy.

Comparative analysis shows Naive Bayes outperforms logistic regression, SVM, and SGD.

Unigram features are most effective for Bengali sentiment classification.

Abstract

Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, are growing enormously among the people. Reader's opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial Naive Bayes with unigram feature outperforms the other techniques with 84% accuracy on the test set.