Analysis through Text Mining Approach: Culinary Experience Dimensions of International Tourists

Abstract

Culinary tourism attracts a large number of international tourists to experience local delicacies. With the availability of internet services, tourists form an impression about the destination by accessing the reviews digitally. This paper aims to determine the culinary experience dimensions by evaluating the boundaries of digital evidences that are written online by the international tourists after experiencing local food at the attraction. The data was gathered from a popular review website TripAdvisor.com which is widely used in travel and tourism industry. A total of 267 reviews from international tourist about Delhi street food were gathered and qualitative analysis was carried out using bigram analysis in R software to identify the frequent phrases. Later, dimensions were classified, based on the frequent phrases. The findings suggested that identified dimensions of tourists experience on Delhi street food can enhance the destination image of the attraction in front of international tourist who is scrutinizing the reviews online. This paper used a novel dataset and the results can contribute to the culinary literature. Future studies can be planned using bigger respondent values at different geographical locations.