Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews

Customer retention has been one of the most recognized research issues in the service industry. The next on the list is predicting customer behaviour or understanding customer intent, which is particularly important for the hospitality and tourism industries. This study investigates the customers' hotel revisiting behaviour using a large-scale customer review data, which can shed light on the potentiality of (i) identifying the factors that are associated with the customer revisiting behaviour and attracting more customers to reuse their services and (ii) predicting future customer revisiting behaviour to a hotel. We analyse the data of 105,126 customers of an online hotel reservation service, and conduct a sentimental analysis on the user feedback reviews. By comparing one-time visitors and re-visitors, our analysis shows that the feedback reviews of re-visitors tend to (i) contain more words in a sentence and (ii) reveal more positive/negative sentiments than those of one-time visitors. On the other hand, the feedback reviews of one-time visitors tended to include more analytical and anxious words than those of re-visitors. The findings can serve as a foundation for the use of big data analysis in hospitality and tourism research.