Enhanced Recommender System with Sentiment Analysis of Review Text and SBERT Embeddings of Item Descriptions

January 3, 2026MathematicsOpen Access

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Key Points

This research aims to enhance recommender systems by combining sentiment analysis with user preferences to address data sparsity.
Developed a recommender system utilizing Amazon dataset.
Employed Sentence-BERT for item attribute embedding.
Conducted sentiment analysis on review text.
Compared performance against traditional baseline models.
Performed ablation studies to evaluate components.
Proposed model significantly outperformed traditional models in recommendation performance.
Combination of item attributes and review sentiments led to improved user preference modeling.
Top K recommendation indicators were enhanced through the integration of quantitative and emotional data.

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This approach improves recommendation performance in online shopping by integrating sentiment analysis and user preferences.

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Lim et al. (2026) studied this question.

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