Sentiment Analysis of Amazon Product Reviews: Leveraging NLP Techniques for Enhanced Classification Accuracy

This work tackles sentiment classification on product reviews using a hybrid LSTM-GRU model. The primary objective is to evaluate the model's ability to correctly categories sentiments (positive, negative, and neutral) in a massive review dataset including 568,454 rows & 10 columns of product reviews. Data collecting from an online retailer's review system forms part of the approach, then additional pre-processing activities including text cleaning, tokenising, and padding for sentiment analysis preparation follow. Aimed to capture intricate sentiment patterns from textual data, the hybrid LSTM-GRU architecture groups the reviews. The model is defined by its accuracy and loss, and its performance is evaluated using metrics such as recall, F1 score, and precision. A test accuracy of 82.50% is achieved by the model, suggesting high sentiment classification performance, with a loss value of 1.56, according to the results. These results suggest potential for real-time sentiment analysis applications in e-commerce systems since they show that the hybrid LSTM-GRU model efficiently detects sentiment trends inside product reviews. The results highlight the great generalising capacity of the model, thereby reducing prediction error and offering correct sentiment classifications over several review data.