Sentiment Analysis of Hotel Guests Using the Naive Bayes Classifier

Abstract

The reviews given by visitors are critical and should be meticulously analyzed to enhance the quality of hotel services. Traditionally, reviewing each guest’s feedback manually is time-consuming and inefficient. Therefore, there is a need for an effective technique to aggregate and analyze large volumes of reviews. This research aims to provide a sentiment analysis of hotel visitor reviews using the Naïve Bayes algorithm. This study's findings are based on its comprehensive approach to automating sentiment analysis, which significantly reduces processing time and increases accuracy. For this purpose, we used Google Colaboratory to implement and evaluate the Naïve Bayes algorithm. The results reveal that the sentiment analysis model achieves varying levels of accuracy across different hotels: Hotel Arena with 0.74, K Hotel George with 0.94, Hotel Claridge Paris with 0.92, Suite Hotel 900 m zur Oper with 0.84, and Atlantis Hotel Vienna with 0.86. These findings underscore the potential of the Naïve Bayes algorithm for effectively capturing and analyzing customer sentiment, offering a valuable tool for hotel management to understand and improve guest satisfaction. This study is pioneering in its application of the Naïve Bayes algorithm in the context of sentiment analysis for multiple hotel brands, providing a scalable solution for the hospitality industry.