Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective

TL;DR

This review discusses recent advancements in sentiment-aware recommendation systems for e-commerce, emphasizing NLP techniques that improve prediction accuracy and explainability by analyzing user feedback.

Contribution

It categorizes recent NLP-based sentiment integration approaches in recommendation systems and highlights future research directions for more effective e-commerce personalization.

Findings

Sentiment analysis enhances recommendation accuracy.

Transformer and graph neural networks improve opinion extraction.

Real-time conversational recommenders adapt to user feedback.

Abstract

E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.