A Study of a Method to Understand the Intention of Taste Expressions through Text Mining
Shinichi Tachibana, Kazuhiko Tsuda · Procedia Computer Science · 2020
The purpose of this study is to evidence a method of understanding the intentions of taste expressions from word-of-mouth data of cooking recipe websites using text mining. This study aims to clarify the use of the word “KOKU” as an example to verify the method. KOKU is one of the taste expressions used like richness experienced in various dishes such as in the taste of wine. In order to clarify the relationship between the features of KOKU and cooking categories, they were clustered using the latent Dirichlet allocation. The categories were classified into groups of foods using similar ingredients, sweetness, oils, and seasonings. Through the analysis mentioned above, the features of KOKU were defined. In the past, there has been no attempt to clarify the features of KOKU using word-of-mouth data from cooking recipe websites. The success in defining “KOKU” is evidence that this method has potential to be extended and applied to expressions other than KOKU.