Analyzing changes in hotel customers' expectations by trip mode

With the emergence of Web 2.0, electronic word-of-mouth (eWOM) shared through social networking sites has become the primary information source for many travelers. An enormous quantity of reviews has been posted by customers, and has become a valuable means by which hoteliers can better understand customer satisfaction and expectations. Efforts have been made to analyze these parameters in terms of customers' backgrounds. Although customer expectations vary according to background, whether or not this is still the case across different trip modes remains unknown. In this study, an eWOM dataset was obtained from an online source and sentiment mining used to improve its quality by imputing missing values. A complete analysis of customer profiles and their contrast by trip mode was then conducted using association rule mining. The empirical results demonstrate differences in both customer expectation and satisfaction when the same traveler engages in different trip modes.