Enhancing Sentiment Classification Accuracy of Amazon Product Reviews via NLP Approaches
Keywords:
Hybrid LSTM-GRU, Sentiment Classification, Product Reviews, Text Preprocessing, AccuracyAbstract
An indispensable tool for businesses, sentiment analysis sifts through customer reviews on e-commerce platforms to reveal vital information into product quality and consumer satisfaction. Using a massive Amazon review dataset with over 568,000 entries over 10 characteristics, this work proposes a strong deep learning method to sentiment classification. Cleaning, tokenisation using a 10,000-word vocabulary and padding are all part of the text data's extensive preprocessing that guarantees consistent input for the models. The majority of evaluations are favourable, showing that customers are generally satisfied, according to the exploratory data analysis. To understand the reviews' sequential relationships and contextual subtleties, we suggest a mixed-layer deep learning model that combines LSTM and GRU layers, with the addition of embedding and dropout techniques. With an accuracy of 96.5% after 100 epochs of training, the model surpasses both standalone GRU models and leading techniques in the past that used topic models and embeddings. Loss, F1-score, recall and accuracy are some of the evaluation indicators that back up the model's efficacy. In e-commerce review analysis, the results show that scalable sentiment classification using a combination of LSTM and GRU architectures with thorough preprocessing is possible.
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