원문정보
초록
영어
It is a general practice to evaluate food taste based on sensory tests, however, this test method’s disadvantage is that a lot of cost and time is required and significant deviation is taken place depending on each evaluator as well. Food taste evaluation by utilizing SNS-based big data for supplementing this disadvantage is considered to be a new challenge and innovative method. The objective of this study is to suggest a system that evaluates and recommends the level of domestic food taste by not only clustering food preference using k-means algorithm after sorting out food-related tweet contents from typical twitter of SNS, followed by scoring taste adjectives being mainly used in daily life by using rough set and selecting food-related adjectives among the scored adjectives, but also exploring the level of salty, sour, savory, bitter and sweet tastes through perception map.
목차
1. Introduction
2. Relevant Research
2.1. Big Data and SNS
2.2. Taste Adjectives
2.3. Rough Set Theory and Clustering
3. Recommendation System Design based on Taste Adjectives
3.1. Outline of Recommendation System
4. Implementation
4.1. Source (Raw) Data
4.2. Morphologic Element Analysis
4.3. Clustering
5. Conclusion
References