An Exploratory Study on the Semantic Network Analysis of Food Tourism through the Big Data

Abstract

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing ‘food tourism’ keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of ‘core service’ and ‘social marketing’ was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; ‘socioeconomic circumstance’ related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named ‘core service’, ‘social marketing’, ‘destinations’ and ‘social environment’. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.