A Sentiment-Driven Deep Learning System for Hospital Recommendation
TL;DR
A novel framework that integrates sentiment analysis on the Yelp dataset using DistilBERT, a lightweight transformer-based language model is proposed that incorporates Neural Collaborative Filtering (NCF) for the recommendation process and addresses sparsity issues in user–item interaction data.
Abstract
A Healthcare Recommender System (HRS) is a personalized decision-support system designed to recommend healthcare-related services, providers, information, advice, diagnoses, treatments, or lifestyle tips to users based on users’ preferences, characteristics, or individualized health data. Healthcare recommendation systems rely on multiple data sources. Among these sources, user reviews and comments on online healthcare platforms and social media serve as a valuable source to provide direct and often experience-based information about users’ satisfaction and opinions regarding healthcare services. Therefore, this study focuses on exploiting sentiment information extracted from user-generated reviews to enhance healthcare service recommendations. We propose a novel framework that integrates sentiment analysis on the Yelp dataset using DistilBERT, a lightweight transformer-based language model. The proposed framework incorporates Neural Collaborative Filtering (NCF) for the recommendation process. It utilizes Singular Value Decomposition (SVD) to address sparsity issues in user–item interaction data, thereby maintaining reliable performance even with limited data availability. The proposed approach achieved a Mean Absolute Error (MAE) of 0.34, a Root Mean Square Error (RMSE) of 0.66, and an Area Under the Curve (AUC) of 0.92. It also demonstrated strong ranking performance, achieving a Recall@10 of 0.74, demonstrating its effectiveness and accuracy in recommendation tasks. Compared with the rating-based, without-SVD, and without-NCF variants, which achieved RMSE values of 0.97, 1.80, and 1.14, respectively, the proposed model consistently achieved better performance. These results highlight the contribution of sentiment analysis, SVD-based interaction augmentation, and NCF to the recommendation performance. These results are promising and confirm the potential of the proposed approach for improving the reliability and performance of recommender systems.