Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation
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 valen...
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Universidad Internacional de La Rioja (UNIR)
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Online Access: | http://www.ijimai.org/journal/bibcite/reference/2767 |
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doaj-363707361c0c4337896bf3bb2706a76a2020-11-25T03:26:26ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602020-06-01621010.9781/ijimai.2020.05.002ijimai.2020.05.002Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual EvaluationWenlin HuangQun WuNilanjan DeyAmira AshourSimon James FongRubén González-CrespoMore 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.http://www.ijimai.org/journal/bibcite/reference/2767clusteringaffective computingfuzzyvalence-arousal-dominanceproduct evaluation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenlin Huang Qun Wu Nilanjan Dey Amira Ashour Simon James Fong Rubén González-Crespo |
spellingShingle |
Wenlin Huang Qun Wu Nilanjan Dey Amira Ashour Simon James Fong Rubén González-Crespo Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation International Journal of Interactive Multimedia and Artificial Intelligence clustering affective computing fuzzy valence-arousal-dominance product evaluation |
author_facet |
Wenlin Huang Qun Wu Nilanjan Dey Amira Ashour Simon James Fong Rubén González-Crespo |
author_sort |
Wenlin Huang |
title |
Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation |
title_short |
Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation |
title_full |
Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation |
title_fullStr |
Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation |
title_full_unstemmed |
Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation |
title_sort |
adjectives grouping in a dimensionality affective clustering model for fuzzy perceptual evaluation |
publisher |
Universidad Internacional de La Rioja (UNIR) |
series |
International Journal of Interactive Multimedia and Artificial Intelligence |
issn |
1989-1660 1989-1660 |
publishDate |
2020-06-01 |
description |
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. |
topic |
clustering affective computing fuzzy valence-arousal-dominance product evaluation |
url |
http://www.ijimai.org/journal/bibcite/reference/2767 |
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