A Hybrid of Syntactic Structure and Contextual Semantics for Enhanced Product Review Sentiment Analysis
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Abstract
With the rapid growth of e-commerce platforms, analyzing customer reviews has become essential for understanding product perception and supporting data-driven decision-making. Traditional sentiment analysis methods often rely solely on sequential text representations, which may overlook the syntactic structures and contextual nuances present in customer feedback. This study aims to develop an interpretable hybrid framework that effectively captures both semantic meaning and syntactic dependencies to enhance sentiment prediction accuracy. The proposed model combines contextual embeddings from SentBERT with syntactic features extracted via Graph Feature Fusion. The process involves preprocessing customer reviews, extracting aspect-based semantic representations using SentBERT, and constructing syntactic graphs via dependency parsing. These features are merged in a fusion layer and refined using supervised contrastive learning to improve class separability in the sentiment space. SHAP Explainable AI is integrated to provide human-interpretable explanations for the sentiment predictions. The hybrid model achieves a sentiment classification accuracy of 97%, outperforming baseline methods, including BERT (93%) and GCN (88%), as well as classical machine learning algorithms. These findings highlight the effectiveness of integrating syntactic and contextual features in sentiment analysis. The framework can be applied to real-world e-commerce platforms to enhance automated review analysis, improve customer service insights, and support product development strategies.
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