Enhancing Sentiment-Driven Recommender Systems With LLM-Based Feature Engineering: A Case Study in Drug Review Analysis
Auteur(s): Kyamakya KyandoghereNom de la revue/Journal: IEEE Access
Mois: juillet
Numéro: DOI: 10.1109/ACCESS.2025.3590326
Année: 2025
pages: 130304 - 130322
Résumé
<p><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">Sentiment analysis is vital for evaluating user feedback in drug reviews because understanding patient experiences leads to more personalized treatment recommendations by providing insights into the real-world effectiveness and tolerability of medications, which are often overlooked in clinical trials. This study evaluates the effectiveness of word-level and sentence-level embeddings for feature extraction in sentiment analysis. These embeddings are used in sequential models (Bi-LSTM, CNN) and non-sequential models (Random Forest, DNN, ExtraTreesClassifier). The Random Forest model with LLM2Vec achieves the best performance, with 0.93 accuracy, F1-scores of 0.95 (positive) and 0.88 (negative), and precision scores of 0.93 (positive) and 0.94 (negative). This approach detects subtle negative feedback often missed by standard models. To capture social consensus in patient feedback, we introduce … </span></p>