Graph Neural Network-Based Sentiment-Driven Real Estate Recommender

Abstract

Objectives: This research work proposes a sentiment-driven real estate recommendation framework. This integrates locality-aware news sentiment, ensemble-based property price prediction, and Graph Neural Network (GNN)-based session recommendation to enhance recommendation relevance and contextual awareness. The proposed framework aims to address the limitations of conventional recommendation systems which primarily rely on static user–item interactions and ignore dynamic environmental factors influencing real estate decisions. Method: Locality-specific news articles, property datasets, and user session data are the major contributors to this system. News sentiment is extracted using Global Vectors for Word Representation (GloVe) embeddings and Latent Dirichlet Allocation (LDA)-based domain clustering. The derived sentiment scores are integrated with real estate features for price prediction using an ensemble regression framework. The framework utilizes Linear Regression, XGBoost, and Random Forest algorithms. Also, Graph Neural Networks are employed to model session-level user interactions for personalized recommendation generation. Findings: The proposed framework has achieved an overall prediction accuracy of 81%. The precision, recall, and F1-score values are 0.80, 0.78, and 0.79, respectively. Comparative analysis with recent state-of-the-art recommendation approaches indicates improved recommendation relevance and contextual adaptability. This was due to the integration of external sentiment information and session-aware graph learning, which significantly improves recommendation relevance and contextual adaptability. This was due to the integration of external sentiment information and session-aware graph learning. Novelty: The major novelty of this work lies in unified integration. Locality-aware news sentiment, ensemble-based property price prediction, and GNN-driven session recommendation are brought within a single real estate recommendation framework. Unlike existing systems, the proposed approach incorporates crime-sensitive and infrastructure-aware locality sentiment to improve recommendation transparency, contextual awareness, and user trust. Keywords: Sentiment Analysis, Real Estate Recommendation, Graph Neural Networks, Ensemble Learning, Session-Based Recommendation, Property Price Prediction