  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.
