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

Mostrar afiliaciones +
Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2020

Volumen: 6

Número: 2

Páginas: 28-37

Tipo: Artículo

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

Otras publicaciones en: IJIMAI

Resumen

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.

Referencias bibliográficas

  • Dey, N., Babo, R., Ashour, A. S., Bhatnagar, V., & Bouhlel, M. S. (Eds.). “Social networks science: Design, implementation, security, and challenges: From social networks analysis to social networks intelligence”, Springer. doi:10.1007/978-3-319-90059-9_1.
  • Chiu, M.-C. and K.-Z. Lin. “Utilizing text mining and Kansei Engineering to support data-driven design automation at conceptual design stage”, Advanced Engineering Informatics vol.38, pp.826-839, 2018, doi:10.1016/j.aei.2018.11.002.
  • Ahmed, S. S., Dey, N., Ashour, A. S., et al. “Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach”, Medical & Biological Engineering & Computing, vol.55, no 1, pp.101-115, 2017, doi: 10.1007/s11517-016-1508-7
  • Han, Z., et al. “Clustering and retrieval of mechanical CAD assembly models based on multi-source attributes information”, Robotics and Computer-Integrated Manufacturing, vol.58, pp.220-229, 2019, doi:10.1016/j.rcim.2019.01.003
  • Lacko, D., et al. “Product sizing with 3D anthropometry and k-medoids clustering”, Computer-Aided Design, vol. 91, pp. 60-74, 2017, doi: 10.1016/j.cad.2017.06.004
  • Li, W., et al. “A Comprehensive Approach for the Clustering of SimilarPerformance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles”, Engineering vol. 5, no. 4, pp. 795-802, 2019, doi: 10.1016/j.eng.2019.07.005
  • Franco, M. A. “A system dynamics approach to product design and business model strategies for the circular economy”, Journal of Cleaner Production, vol. 241, 118327, 2019, doi:10.1016/j.jclepro.2019.118327
  • Chick, C. F. “Cooperative versus competitive influences of emotion and cognition on decision making: A primer for psychiatry research”, Psychiatry Research, vol. 273, pp. 493-500, 2019, doi: 10.1016/j. psychres.2019.01.048.
  • Mhetre, N. A., Deshpande, A. V., & Mahalle, P. N. “Trust management model based on fuzzy approach for ubiquitous computing”, International Journal of Ambient Computing and Intelligence, vol. 7, no.2, pp. 33-46, 2016, doi: 10.4018/978-1-5225-9866-4.ch022.
  • Pleyers, G. “Shape congruence in product design: Impacts on automatically activated attitudes”, Journal of Retailing and Consumer Services, 101935, 2019, doi: 10.1016/j.jretconser.2019.101935.
  • Balakrishnan, J., et al. “Product recommendation algorithms in the age of omnichannel retailing -An intuitive clustering approach”, Computers & Industrial Engineering, vol. 115, pp. 459-470, 2017, doi: 10.1016/j. cie.2017.12.005.
  • Büyüközkan, G. and M. Güler. “Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique”, Measurement vol. 153, 107353, 2019, doi: 10.1016/j.measurement.2019.107353.
  • Lipor, J. and L. Balzano, “Clustering quality metrics for subspace clustering”, Pattern Recognition, 107328, 2020, doi: 10.1016/j. patcog.2020.107328.
  • Pandey, M. M. “Evaluating the strategic design parameters of airports in Thailand to meet service expectations of Low-Cost Airlines using the Fuzzy-based QFD method”, Journal of Air Transport Management, vol. 82, 101738, 2019, doi: 10.1016/j.jairtraman.2019.101738.
  • Qiao, W., et al. “A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS”, Ocean Engineering, vol. 197, 106892, 2019, doi: 10.1016/j. oceaneng.2019.106892.
  • Hsu, C.-C., et al. “Relationship between eye fixation patterns and Kansei evaluation of 3D chair forms”, Displays, vol. 50, pp. 21-34, 2017 doi: 10.1016/j.displa.2017.09.002.
  • Cambria, E., Poria, S., Hussain, A., & Liu, B. “Computational Intelligence for Affective Computing and Sentiment Analysis”, IEEE Computational Intelligence Magazine, vol. 14, no. 2, pp. 16-17, 2019, doi: 10.1109/ MCI.2019.2901082.
  • Gonzalez CB, García-Nieto J, Navas-Delgado I, Aldana-Montes JF, “A Fine Grain Sentiment Analysis with Semantics in Tweets”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no.6 pp. 22-28, 2016, doi: 10.9781/ijimai.2016.363.
  • Kossaifi, J., et al. “AFEW-VA database for valence and arousal estimation in-the-wild”, Image and Vision Computing, vol. 65, pp.23-36, 2017, doi: 10.1016/j.imavis.2017.02.001.
  • Jaeger, S. R., et al. “Valence, arousal and sentiment meanings of 33 facial emoji: Insights for the use of emoji in consumer research”, Food Research International, vol. 119, pp. 895-907, 2018, doi: 10.1016/j. foodres.2018.10.074.
  • Nalepa, G. J., et al. “Affective computing in ambient intelligence systems”, Future Generation Computer Systems, vol. 92, pp. 454-457, 2018, doi: 10.1016/j.future.2018.11.016.
  • Trabelsi, I., Bouhlel, M. S., & Dey, N. “Discrete and continuous emotion recognition using sequence kernels”, International Journal of Intelligent Engineering Informatics, vol. 5, no. 3 pp.194-205, 2017, doi:10.1504/ IJIEI.2017.086608.
  • Sarkar, D., Debnath, S., Kole, D. K., & Jana, P. “Influential Nodes Identification Based on Activity Behaviors and Network Structure with Personality Analysis in Egocentric Online Social Networks”, International Journal of Ambient Computing and Intelligence, vol.10 no.4, pp.1-24, 2019, doi:10.4018/IJACI.2019100101.
  • Pass-Lanneau, A., et al. “Perfect graphs with polynomially computable kernels”, Discrete Applied Mathematics, vol. 272, pp. 69-74, 2018, doi: 10.1016/j.dam.2018.09.027.
  • Person, O., et al. “Should new products look similar or different? The influence of the market environment on strategic product styling”, Design Studies, vol. 29, no. 1, pp. 30-48, 2007, doi: 10.1016/j.destud.2007.06.005
  • He, T., et al. “Curvature manipulation of the spectrum of Valence-Arousalrelated fMRI dataset using Gaussian-shaped Fast Fourier Transform and its application to fuzzy KANSEI adjectives modeling”, Neurocomputing, vol. 174, pp. 1049-1059. 2015, doi: 10.1016/j.neucom.2015.10.025.
  • Lee, W. and M. D. Norman. “Affective Computing as Complex Systems Science”, Procedia Computer Science, vol. 95, pp. 18-23, 2016, doi: 10.1016/j.procs.2016.09.288.
  • Salgado, S. and O. S. Kingo. “How is physiological arousal related to self-reported measures of emotional intensity and valence of events and their autobiographical memories?”, Consciousness and Cognition, vol. 75, 102811, 2019, doi: 10.1016/j.concog.2019.102811.
  • Scult, M. A. and A. R. Hariri. “A brief introduction to the neurogenetics of cognition-emotion interactions”, Current Opinion in Behavioral Sciences, vol. 19, pp.50-54, 2017, doi: 10.1016/j.cobeha.2017.09.014.
  • Wang, W. M., et al. “Multiple affective attribute classification of online customer product reviews: A heuristic deep learning method for supporting Kansei engineering”, Engineering Applications of Artificial Intelligence, vol. 85, pp. 33-45, 2019, doi: 10.1016/j.engappai.2019.05.015.
  • Zabotto, C. N., et al. “Automatic digital mood boards to connect users and designers with kansei engineering”, International Journal of Industrial Ergonomics, vol. 74, 102829, 2019, doi: 10.1016/j.ergon.2019.102829.
  • Hyun, K. H. and J.-H. Lee. “Balancing homogeneity and heterogeneity in design exploration by synthesizing novel design alternatives based on genetic algorithm and strategic styling decision”, Advanced Engineering Informatics, vol. 38, pp. 113-128, 2018, doi: 10.1016/j.aei.2018.06.005.
  • Kratzwald, B., et al. “Deep learning for affective computing: Text-based emotion recognition in decision support”, Decision Support Systems, vol. 115, pp. 24-35, 2018, doi: 10.1016/j.dss.2018.09.002.
  • Vučetić, M., et al. “Fuzzy functional dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases”, Engineering Applications of Artificial Intelligence, vol. 88, 103395, 2019, doi: 10.1016/j.engappai.2019.103395.
  • Bagherinia, A., et al. “Reliability-Based Fuzzy Clustering Ensemble”, Fuzzy Sets and Systems, 2020, doi: 10.1016/j.fss.2020.03.008.
  • Chen, Z., et al. “Explore and evaluate innovative value propositions for smart product service system: A novel graphics-based rough-fuzzy DEMATEL method”, Journal of Cleaner Production, vol. 243, 118672, 2020, doi:10.1016/j.jclepro.2019.118672.
  • Eustáquio, F. and T. Nogueira. “Evaluating the numerical instability in fuzzy clustering validation of high-dimensional data”, Theoretical Computer Science, vol. 805, pp. 19-36, 2019, doi: 10.1016/j.tcs.2019.10.039.
  • Hernández-Julio, Y. F., et al. “Fuzzy clustering and dynamic tables for knowledge discovery and decision-making: Analysis of the reproductive performance of the marine copepod Cyclopina sp”, Aquaculture, 735183, 2020, doi: 10.1016/j.aquaculture.2020.735183.
  • Li, W., et al. “Boosted K-nearest neighbor classifiers based on fuzzy granules”, Knowledge-Based Systems 105606, 2020, doi: 10.1016/j. knosys.2020.105606.
  • Rani, P., et al. “A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection”, Journal of Cleaner Production, vol. 257, 120352, 2020, doi: 10.1016/j. jclepro.2020.120352.
  • García-Díaz, Vicente, Jordán Pascual Espada, Rubén González Crespo, B. Cristina Pelayo G-Bustelo, and Juan Manuel Cueva Lovelle. “An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis”, Applied Soft Computing, vol. 67, pp. 822-833, 2018, doi: 10.1016/j.asoc.2017.05.038.
  • Naik, Anima, Suresh Chandra Satapathy, Amira S. Ashour, and Nilanjan Dey. “Social group optimization for global optimization of multimodal functions and data clustering problems”, Neural Computing and - 37 - Regular Issue Applications, vol. 30, no. 1, pp. 271-287, 2018, doi: 10.1007/s00521-016- 2686-9.
  • Hamzaoui, Y., Amnai, M., Choukri, A., & Fakhri, Y. “Novel clustering method based on K-medoids and mobility metric”, International Journal of Interactive Multimedia and Artificial Intelligence, vol.5, no.1, pp. 29- 33, 2018. doi: 10.9781/ijimai.2017.11.001.
  • Cao, Luying, Nilanjan Dey, Amira S. Ashour, Simon Fong, R. Simon Sherratt, Lijun Wu, and Fuqian Shi. “Diabetic plantar pressure analysis using image fusion”, Multimedia Tools and Applications, vol. 79, pp.11213-11236, 2020, doi: 10.1007/s11042-018-6269-x.
  • Mondragon, Victor M., Vicente García-Díaz, Carlos Porcel, and Rubén González Crespo. “Adaptive contents for interactive TV guided by machine learning based on predictive sentiment analysis of data”, Soft Computing, vol. 22, no. 8, pp. 2731-2752, 2018, doi: 10.1007/s00500- 017-2530-x.
  • Tian, Zongmei, Nilanjan Dey, Amira S. Ashour, Pamela McCauley, and Fuqian Shi. “Morphological segmenting and neighborhood pixelbased locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study”, Neural Computing and Applications, vol. 30, no. 12 pp. 3733-3748, 2018, doi: 10.1007/s00521- 017-2955-2.
  • Li, Zairan, Kai Shi, Nilanjan Dey, Amira S. Ashour, Dan Wang, Valentina E. Balas, Pamela McCauley, and Fuqian Shi. “Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction”, Neural Computing and Applications, vol. 28, no. 3, pp. 613-630, 2017, doi:10.1007/s00521-016-2707-8.
  • Pourvali, Mohsen, Salvatore Orlando, and Hosna Omidvarborna. “Topic models and fusion methods: a union to improve text clustering and cluster labeling”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 4, pp. 28-34, 2019, doi:10.9781/ijimai.2018.12.007.
  • Shinde, Gitanjali Rahul, and Henning Olesen. “Beacon-Based Cluster Framework for Internet of People, Things, and Services (IoPTS)”, International Journal of Ambient Computing and Intelligence, vol. 9, no. 4, pp. 15-33, 2018, doi:10.4018/IJACI.2018100102.
  • Ali, Md Nawab Yousuf, Md Golam Sarowar, Md Lizur Rahman, Jyotismita Chaki, Nilanjan Dey, and João Manuel RS Tavares. “Adam deep learning with SOM for human sentiment classification.” International Journal of Ambient Computing and Intelligence, vol.10, no. 3, pp. 92-116, 2019, doi:10.4018/IJACI.2019070106.
  • Sengupta, Diganta. “Taxonomy on Ambient Computing: A Research Methodology Perspective”, International Journal of Ambient Computing and Intelligence, vol.11, no. 1, pp.1-33, 2020, doi:10.4018/ IJACI.2020010101.
Fuente de los datos: Dialnet