Analysis and Prediction of Customer Sentiment for Real Estate Organizations Using Machine Learning Approaches

Abstract

The purpose of the current study was to analyse and predict of customer sentiment towards real estate organizations using Machine Learning Approaches (ML) approaches. The study employed five ML models to predict customer sentiment toward real estate organizations. The data was collected from Yelp.com and filtered using the “Real Estate” tag. The findings revealed that all four machine learning models effectively classified the review text. The logistic regression (LR) and support vector machine (SVM) models achieved the highest accuracy scores at 93.1 and 93.16, respectively. This study emphasizes the potential of machine learning in improving customer experience and facilitating better decision-making in the real estate industry. The results can guide real estate organizations in understanding customer opinions and preferences, enabling them to make necessary improvements. Additionally, the findings have social implications, as they can enhance products, services, and overall customer experiences, leading to increased satisfaction and trust in the industry.