Extracting Kansei Information and Building Empathy in Consumer Vocabularies Using Connectionist Models

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K04887
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 25010:Social systems engineering-related
• Research Institution: Chiba Institute of Technology
• Principal Investigator: Fumiaki Saitoh 千葉工業大学, 先進工学部, 准教授 (30625132)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: 顧客満足度 / 機械学習 / テキストマイニング / データマイニング

Outline of Final Research Achievements

This study is based on data analysis of "voice of customer " as typified by word-of-mouth data and satisfaction surveys, and attempts to extract Kansei information such as evaluation expressions and sensitivity information on products and services using elemental technologies of AI, which has been remarkably developed in recent years. Furthermore, we have obtained new analysis results and developed new analysis approaches by changing the analysis points, such as postings about people's dissatisfaction, local needs, and sensory expressions.

Free Research Field

機械学習

Academic Significance and Societal Importance of the Research Achievements

本研究では，機械学習の応用研究として顧客の感性を対象としたマーケティング課題に取り組んできた．近年ではWebを通じて大規模な顧客情報を取得できることから，これらを意思決定において有効活用することは重要な視点であり，機械学習の新たな応用対象の開拓とその有用性の確認ができたといえる．さらに顧客価値観や感性的な言語表現に関する研究としても新たなアプローチを提案できたことから，本研究は有益な取り組みであったといえる．