A Study on a Method to Understand the Intention of Taste Expressions from Word-of-Mouth Data of Cooking Recipe Sites using Text Mining and Statistical Analysis

Abstract

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. As a result of applying the prediction results of a machine learning model to LIME, using word-of-mouth data including KOKU and SAPPARI as a data set, it was found that the characteristic ingredients contributing to the KOKU were identified. As a result of the analysis, which food composition data is used, it was confirmed that there was a statistically significant difference in lipids and other components of the food ingredients that were identified as the characteristic words of KOKU and SAPPARI. We will conduct further verification by increasing the number of target data based on this method.