Knowledge Discovery for Tourism Using Data Mining and Qualitative Analysis

Abstract

This paper aims to propose a new guideline for analyzing tourist profiles as found in www.tripadvisor.com. These have been examined from two different aspects so as to gain conclusive results. Tourist data were “crawled” from tripadvisor.com through a specific web crawler. Mining techniques using a combination of visualization, clustering, and association rules were instrumental in discovering the first set of interesting knowledge. This was followed by a qualitative analysis applied through Nvivo software via coding of the tourist's comments in order to define the design of the prospective model. A final set of results was obtained once both results confirmed each other. In this study, results show that there are several types of tourists; with each group having different preferences. For example: male Singaporean visitors to hotels tend to enjoy wine and food in addition to outdoor activities; while local visitors to Legoland are not satisfied with certain aspects, such as the price of food. International tourists, however, consider the affirmative points of Legoland. This research can be very useful for tourist associations and hotel managers in Johor Bahru.