Anomaly Detection in Hotel Reviews: Applying Data Science for Enhanced Review Integrity

As the hotel industry increasingly relies on online reviews to attract and retain customers, distinguishing between genuine and fraudulent feedback has become critical. This paper presents a methodology for anomaly detection in hotel reviews by employing advanced exploratory data analysis (EDA). Key aspects of our analysis include evaluating sentiment and review similarity, uncovering unusual behavioral patterns, and identifying patterns in temporal posting trends and distributions. Time series analysis helps to recognize unusual spikes and shifts in user review activity. Additionally, we address issues such as misleading titles and ratings by tracing correlations among crucial review elements. This methodology provides actionable insights and recommendations for improving review filtering processes and addressing fraudulent content, offering a comprehensive analysis of online review patterns without relying on predictive modeling.