Adjectives grouping in a dimensionality affective clustering model for fuzzy perceptual evaluation

  1. Wenlin Huang 1
  2. Qun Wu 2
  3. Nilanjan Dey 3
  4. Amira S. Ashou 4
  5. Simon James Fong 5
  6. Rubén González Crespo 6
  1. 1 Wenzhou Business College
  2. 2 Zhejiang Sci-Tech University
    info

    Zhejiang Sci-Tech University

    Hangzhou, China

    ROR https://ror.org/03893we55

  3. 3 Techno International New Town, West Bengal (India)
  4. 4 Tanta University
    info

    Tanta University

    Tanda, Egipto

    ROR https://ror.org/016jp5b92

  5. 5 University of Macau
    info

    University of Macau

    Macao, Macao

    ROR https://ror.org/01r4q9n85

  6. 6 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

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Journal:
IJIMAI

ISSN: 1989-1660

Year of publication: 2020

Volume: 6

Issue: 2

Pages: 28-37

Type: Article

DOI: 10.9781/IJIMAI.2020.05.002 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: IJIMAI

Abstract

More and more products are no longer limited to the satisfaction of the basic needs, but reflect the emotional interaction between people and environment. The characteristics of user emotions and their evaluation scales are relatively simple. This paper proposes a three-dimensional space model valence-arousal-dominance (VAD) based on the theory of psychological dimensional emotions. It studies the clustering and evaluation of emotional phrases, called VAdC (VAD-dimensional clustering), which is a kind of the affective computing technology. Firstly, a Gaussian Mixture Model (GMM) based information presentation system was introduced, including the type of the presentation, such as single point, plain, and sphere. Subsequently, the border of the presentation was defined. To increase the ability of the proposed algorithm to handle a high dimensional affective space, the distance and inference mechanics were addressed to avoid lacking of local measurement by using fuzzy perceptual evaluation. By comparing the performance of the proposed method with fuzzy c-mean (FCM), k-mean, hard -c-mean (HCM), extra fuzzy c-mean (EFCM), the proposed VADdC performs high effectiveness in fitness, inter-distance, intra-distance, and accuracy. The results were based on the dataset created from a questionnaire on products of the Ming style chairs online evaluation system.

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