This research presents a thorough and systematic approach to classifying the sentiment of Amazon product evaluations using a deep learning framework based on a bidirectional reinforcement learning network. Utilising a large-scale dataset that has been structured-preprocessed into sentiment labels, the study effectively incorporates numerical ratings and review language. Semantic embedding using previously trained GloVe vectors to a text normalisation, and meticulous data cleaning are some of the methods used in the study to guarantee that the model can grasp all the nuances and context-dependent information in language. Because it efficiently regulates the bidirectional flow of information, BiLSTM is necessary for the model to learn via past and future word correlations inside a review. To prevent overfitting and enhance generalisation, the design is fine-tuned with the use of nationwide pooling, batch normalization, including dropout layers. Stratified sampling is a must for data division in order to ensure adequate representation across sentiment classes, especially considering the inherent bias in players material. Performance evaluation using metrics like F1-score, recall, accuracy, and precision shows that the suggested model is effective. The accuracy indicator (0.9124) and the F1-score (0.9123) stand out among the others, both of which have very high values. The methodology has demonstrated its value by reliably labelling assessments as positive, neutral, or negative. Full exploratory data analysis also shows that class balance and preprocessing have a major effect on the model's performance. Finally, this study lays out a scalable and extensible deep learning-based method that works well in sentiment analysis for e-commerce and customer feedback systems, among other real-world uses.
Shyamol Banerjee (Thu,) studied this question.