Bengali sentiment analysis based on product reviews: unveiling consumer voices

Abstract

"These days, customers are more keen to buy products online rather than going to a shop or market. However, they often fear about the quality of products, as there is no way to measure them before buying them. As a result, most buyers rely on the reviews of other customers who have already purchased the product. For this reason, customer reviews are very crucial for the e-commerce industry. A popular method for assessing the quality of a product is opinion mining, which is also called sentiment analysis. It is a method of extracting emotion from a text using natural language processing (NLP). In this study, we have collected data from an e-commerce site named Daraz and introduced a new dataset that contains Bengali reviews. A total of 48000 reviews were collected, of which 22000 were Bengali. 15000 are in English, while the rest of 9000 are in “Banglish” (Romanized Bengali). Several data preprocessing techniques were used to introduce a new clean dataset that only contains Bengali reviews. Five machine learning algorithms—Naive Bayes, Random Forest, Gradient Boosting Classifier, Logistic Regression, and Support Vector Machine (SVM)—and three deep learning models—BiLSTM, Multilingual BERT, and BanglaBERT—were implemented to evaluate our work. Our work should help the sellers filter out the best products that are popular among consumers. "