Sentiment analysis of Bangla customer reviews on daraz using deep learning approach

Abstract

This study examines the effectiveness of deep learning models for sentiment analysis of Bangla customer evaluations on the Daraz platform. The researchers developed and evaluated various models, including CNN, LSTM, GRU, and a hybrid CNN+BiLSTM, focusing on their ability to accurately classify sentiments. The experimental setup involved exhaustive preprocessing of Bangla text and using TensorFlow and PyTorch frameworks for model training. The CNN+BiLSTM model achieved the highest accuracy and precision, indicating its superior performance in identifying positive sentiments. The CNN model showed balanced performance with high accuracy and F1-Score, making it reliable for general sentiment classification tasks. The CNN+BiLSTM model was the most effective for precision sentiment predictions, while the CNN model proved a reliable choice for balanced sentiment analysis. The research aims to construct sentiment analysis algorithms for multilingual e-commerce platforms, as online stores like Daraz have a significant amount of customer feedback, making these critiques more credible than other forms of advertising material.