Using Social Media Mining to Understand Public Opinion towards Destination Image

Abstract

In the middle of highly competitive tourism market, development of successful destination image is paramount towards memorable experience for visitors. This study aims to support tourism stakeholders from the service providers and national tourism by analyzing, and extracting meaningful patterns from social media, e.g. Twitter, based on destination image information. This data plays an important role for destination marketers to distinguish their destination among others based on Twitter Statistics and key public opinion towards destination image attributes. London and New York were used as destination cities under the analysis of text mining with the concept linkage approach. Results shows five distinct keywords attributed to each city. Each keyword found to be relevant in representing the image of destination cities based on the public opinion on Twitter. For keyword “Culture and Cultural”, term "British” and "Black" represent London and New York the best, respectively. In keyword “Entertainment”, term "James Bond" and "Broadway" represent London and New York, respectively. In keyword “Festival”, term "Lumiere" and “Global Citizen Festival” are best in describing city of London and New York, respectively. In keyword “Food”, term "traditional British food" best describes London and "Food truck" best describes New York. The keyword “Shopping” exhibits term "Etsy" as the image of London and “Kate Spade" as the image for New York. This research reveals the value of social media analysis and the ability of text mining as an effective technique to extract opinions from vast amount of available social media data. Recommendations related to tourism strategic plan are made to facilitate possible future destination image studies.