A Comparative Study of Machine Learning Approaches for Sentiment Classification of Bengali Book Reviews Using Word2Vec

This study explores sentiment analysis of Bengali book reviews, addressing a critical research gap in analyzing Bengali text for this domain. Customer reviews and ratings significantly influence online platforms, and automated sentiment analysis offers an efficient method to understand user emotions and opinions. To facilitate this, a dataset of 16,600 Bengali reviews was developed by compiling three datasets and augmenting them with data from online bookstores and Facebook pages. The Word2Vec technique was utilized for feature extraction, employing Continuous Bag of Words (CBOW) and Skip-gram models. Various machine learning classifiers were a pplied to classify sentiment polarity as positive or negative, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), AdaBoost, XGBoost, Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), and a Hybrid model. The Skip-gram model paired with the Hybrid classifier achieved t he highest accuracy of 92%. Furthermore, classifiers were e valuated using metrics such as accuracy, recall, precision, Fl-score, and ROC curves. This research advances sentiment analysis for Bengali text, demonstrating effective techniques for predicting sentiment and contributing to broader natural language processing applications for underrepresented languages.