Abstract:
This research focuses on sentiment analysis of Bengali e-commerce comments using
various machine learning algorithms. The primary objective is to develop and evaluate
models that can accurately classify comments into positive or negative sentiments. I
experimented with LR, DT, RF, MNB, KNN, SVM, and SGD. My dataset, collected
through web scraping, underwent extensive preprocessing to ensure quality and
relevance. The performance of each model was assessed using metrics such as
accuracy, precision, recall, and F1-score. Among all the models, SVM exhibited the
best performance, achieving a training accuracy of 89.6%, validation accuracy of
87.6%, and a model accuracy of 86.7%. This superior performance indicates SVM's
robustness and effectiveness in handling high-dimensional data and non-linear decision
boundaries in the context of sentiment analysis for Bengali text. In addition to model
training and evaluation, I implemented a web application for practical deployment,
designed using HTML, CSS, JavaScript, and the Flask framework. This application
facilitates user-friendly interaction and real-time sentiment analysis, ensuring
scalability and reliability. The integration of Explainable AI (XAI) techniques further
enhances the interpretability of the model's predictions, providing insights into the
factors influencing sentiment classification. Overall, my study demonstrates the
potential of SVM in achieving high accuracy and reliability in sentiment analysis of
Bengali e-commerce comments, paving the way for more advanced applications in
natural language processing for underrepresented languages.
