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Area of Science:

  • Human-Computer Interaction
  • Product Design
  • Affective Computing

Background:

  • Product design increasingly requires satisfying customer affective experiences.
  • Kansei engineering (or Kansei design) focuses on developing products that meet these emotional needs.
  • Selecting relevant affective variables is crucial for understanding customer emotions in Kansei design.

Purpose of the Study:

  • To develop an efficient methodology for extracting affective variables from user data.
  • To effectively capture users' implicit needs through affective variable analysis.
  • To enhance the understanding of customer emotions in product design.

Main Methods:

  • Extraction of affective variables from online customer reviews.
  • Classification of extracted variables using a self-organizing map (SOM).
  • Verification through a product experiment on recliners using Amazon.com data.

Main Results:

  • 'Comfort' was identified as the most frequent affective variable for recliners.
  • 15 distinct clusters of affective experiences related to recliners were classified using SOM.
  • Text mining and SOM proved effective for analyzing customer affective experiences.

Conclusions:

  • The developed methodology efficiently extracts and analyzes customer affective variables.
  • Findings provide insights into customer emotions regarding recliners.
  • Text mining and SOM are valuable tools for understanding user affective experiences in product design.