Enhancing Hotel Services Through Sentiment Analysis

Sentiment analysis in hotel reviews has become a prominent field of research in computer science, closely tied to machine learning and deep learning. The surge of online platforms like TripAdvisor, Booking, and Yelp has led to active sharing of consumer experiences and emotions regarding hotels, generating vast amounts of data. Researchers employ a range of techniques, including traditional machine learning techniques such as SVM, Naive Bayes and Logistic Regression, alongside deep learning models like RNN, LSTM, and BERT, to evaluate their efficacy in extracting customer emotions related to hotel services. The findings consistently demonstrate that BERT often surpasses traditional methods in both precision and contextual understanding. This advancement empowers hotel businesses to better cater to their customers’ needs and significantly enhance overall satisfaction.