Deceptive Reviews Detection Using Machine Learning Techniques in the Hotel Industry

In the realm of purchasing and business decisions, the significance of online reviews has soared, particularly within the hotel industry. The authenticity of these reviews is paramount, yet suspicions of deception have emerged, casting a shadow over their reliability. The recognition of deceptive reviews has gained traction, posing a potential threat to shaping readers’ opinions and steering their purchase choices. To counter this issue, this paper introduces a sophisticated machine learning predictive model tailored for assessing hotel reviews’ genuineness. Employing Support Vector Machine, Logistic Regression, and Multinomial Naïve Bayes models, an exhaustive experimentation was conducted. The empirical findings underscore the supremacy of the Support Vector Machine, boasting an impressive 90% accuracy in discerning deceptive reviews—a notable achievement in enhancing the reliability of online feedback.