Intelligent Hotel Recommendation System Using Sentiment Classification and Machine Learning

Abstract

Travellers are depending more and more on digital platforms to select appropriate lodgings as their dependence on online evaluations grows. However, current hotel recommendation systems sometimes offer general recommendations without being able to comprehend user-specific requirements or evaluate reviews according to specific service elements like location, cleanliness, food quality, and security. Additionally, these systems frequently ignore neutral thoughts, which lowers the recommendations' accuracy and personalization. This study's goal is to create an intelligent hotel recommendation system that combines cutting- edge machine learning and sentiment analysis methods to provide tailored, aspect-based recommendations. To achieve robust classification, the suggested model combines Random Forest with BERT (Bidirectional Encoder Representations from Transformers) for deep contextual sentiment interpretation. To handle a sizable dataset of hotel reviews gathered from Booking.com, the system uses natural language processing techniques including lemmatization, tokenization, stop-word removal, and feature extraction using TF-IDF. By classifying and analyzing reviews based on different hotel features, consumers may do query-based filtering. For instance, they might request hotels with high ratings for cleanliness or food quality. Common issues with current systems, such as cold- start issues, a lack of aspect-level insights, and inadequate user personalization, are addressed by this hybrid paradigm. The method improves the accuracy and applicability of hotel suggestions by precisely reading user preferences and attitudes. The study shows how deep learning and ensemble techniques may be used to create recommendation systems that are more context-aware and user- centric, which enhances decision- making in actual travel situations