What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management

Abstract

Recently, the Internet has brought a big change in tourists' behavior patterns. Travelers not only reserve hotels and airline tickets online, but also exchange travel information and descriptions of pleasant or unpleasant travel experiences through online review sites and personal travel blogs. In spite of the increasing use of online channels, application of online text data has been limited since the volume of the data set is too large to analyze manually and comprehensively. With recent technological advances in processing big data online, consumer-generated information can be automatically analyzed by artificial intelligence. As an aspect of smart tourism, this study applied the sentiment analysis method to analyze travelers' online reviews of Paris. A total of 19,835 pieces of review data collected from a traveler review site (www.virtualtourist.com) were processed. All reviews were grouped into 14 categories as follows: overview, restaurants, sightseeing, hotels, things to do, night life, transportation, shopping, sporting & outdoors, favorites, off the beaten path, what to pack, tourist traps, warnings and danger, and local customs. Tourists' perception about the service in each category was successfully measured, and as an illustration, we chose “transportation” category that reported relatively low level of service quality for post-hoc analysis to reveal why tourists feel negatively about the transportation service. © 2017 Elsevier Inc.