Article · Information · April 22, 2026

Enhanced Sentiment Analysis of E-Commerce Product Reviews Using Luong Attention-Based Bi-LSTM

Orken J. Mamyrbayev, Dinara Mussayeva, Turdybek Kurmetkan

Abstract

The rapid growth of e-commerce has highlighted the critical need for efficient customer review sentiment analysis, yet natural language complexities like sarcasm and mixed sentiments remain challenging. To address these ambiguities, this study proposes a novel sentiment analysis architecture. The methodology integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network with a Luong Attention mechanism. The Bi-LSTM component models the sequential and bidirectional context of the text, while the Luong Attention mechanism isolates and emphasizes the most significant parts of the reviews for precise sentiment detection. The proposed hybrid model demonstrates exceptional performance compared to traditional methods, achieving an accuracy of 96.67%, a precision of 96.83%, and a recall of 96.67%, alongside relatively low overfitting. Ultimately, the findings confirm that this architecture effectively manages ambiguous language and is highly capable of large-scale, real-time sentiment analysis, offering robust analytical tools for shaping e-commerce marketing strategies.

Sentiment Analysis and Opinion MiningTopic ModelingStock Market Forecasting Methodssentiment analysise-commerce product reviewsbidirectional LSTM

Enhanced Sentiment Analysis of E-Commerce Product Reviews Using Luong Attention-Based Bi-LSTM

Abstract

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