Leveraging Word2Vec-Enhanced CNN-LSTM Hybrid Architecture for Sentiment Analysis in E-Commerce Product Reviews

Abstract

The amalgamation of machine learning (ML) techniques and natural language processing (NLP) is leveraged to evaluate the sentiment of textual input. With the increasing popularity of e-commerce platforms like Amazon, product reviews have emerged as an essential source of information for potential purchasers, providing insights into product quality and performance from the consumers' viewpoints. This study aims to systematically organize and analyze customer opinions to effectively capture consumer sentiment based on product reviews. In this study, we propose a deep learning framework that combines a stacked 1D convolutional layer (CNN) with a Long Short-Term Memory (LSTM) network, using pre-trained Word2Vec embedding as fixed input representations. Evaluated on a large Amazon product review dataset, our model — StackedCNN-LSTM-W2V — achieves a classification accuracy of 99%, outperforming traditional CNN, LSTM, and logistic regression baselines.