Abstract

 In today’s competitive hospitality industry, customer reviews significantly impact a hotel’s reputation, revenue, and long-term success. With the exponential growth of online review platforms, manually analyzing customer feedback has become inefficient and error-prone. Customer reviews play a crucial role in shaping a hotel’s reputation and success. However, manual analysis of vast online reviews is inefficient and prone to errors. This research presents a secure and accurate hotel review analysis system using deep learning to extract insights and predict business performance. The system leverages NLP, Sentiment Analysis, and Machine Learning to classify reviews, detect fake feedback, and forecast customer satisfaction trends.The proposed model employs BERT and LSTM for sentiment classification, alongside Naïve Bayes, Random Forest, and SVM for comparative analysis. A cloud-based architecture ensures real-time processing, secure data storage, and encrypted authentication for privacy compliance. Experimental results demonstrate 86% accuracy in sentiment classification and business trend prediction, allowing hotels to optimize strategies for pricing, service quality, and customer retention.This study highlights the impact of AI-driven review analysis in enhancing decision-making for the hospitality industry. Future improvements include reinforcement learning, multilingual support, and integration with voice-based reviews to expand global applicability.