{"id":3697,"date":"2026-04-12T22:43:21","date_gmt":"2026-04-12T14:43:21","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3697"},"modified":"2026-04-26T20:10:37","modified_gmt":"2026-04-26T12:10:37","slug":"jase-202609-32-006","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-006","title":{"rendered":"Artificial Intelligence-Based Consumer Emotion Modeling: Quantifying the Interplay between Self-Depletion, Emotional Compensation and Shopping Addiction"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Haiyang wu<\/i><\/a><\/p>\n\n\n\n

School of Digital Commerce and TradeChangzhou Vocational Institute of Textile and GarmentChangzhou 213164, Jiangsu, China.<\/p>\n<\/div>\n\n\n\n

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Received: December 13, 2025
Accepted: March 22, 2026
Publication Date: April 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

DEL-GRU-Transformer Architecture <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.006<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper proposes an AI framework analyzing psychological factors in online shopping addiction and compulsive purchasing behavior. This study utilizes an openly available Mendeley dataset containing behavioural, demographic, and emotional indicators of women\u2019s online shopping behaviour. The DEL-GRU-Transformer model extracts emotional embeddings, models sequences, and captures context, achieving 97% accuracy. Results show strong classification of positive, neutral, negative, and stress-driven emotions with low error rates and high Area Under the Curve (AUC). The framework helps understand psychological triggers of compulsive online shopping and supports future prevention and personalized intervention strategies.<\/p>\n\n\n\n

Keywords: Consumer Emotion Modelling; Dense Emotional Layer (DEL); GRU-Transformer Hybrid Model; Online Shopping Addiction<\/em><\/p>\n\n\n\n

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  1. [1] V. Vrhovac, S. Vasi\u0107, S. Milisavljevi\u0107, B. Dudi\u0107, P. \u0160tar\u010do\u0148, and M. \u017di\u017eakov, (2023) \u201cMeasuring E-Commerce User Experience in the Last-Mile Delivery\u201d Mathematics 11(6): 1482. DOI: 10.3390\/math11061482.<\/li>\n
  2. [2] P. Cuesta-Vali\u00f1o, P. Guti\u00e9rrez-Rodr\u00edguez, B. Garc\u00eda-Henche, and E. N\u00fa\u00f1ez-Barriopedro, (2024) \u201cThe impact of corporate social responsibility on consumer brand engagement and purchase intention at fashion retailers\u201d Psychology and Marketing 41(3): 649\u2013664. DOI: 10.1002\/mar.21940.<\/li>\n
  3. [3] X. Yang, B. Lai, and C. Tang, (2023) \u201cExperiential Product Promotions on e-Commerce Platform: From the Perspective of Consumer Cognition and Emotion\u201d SAGE Open 13(1): 21582440231153857. DOI: 10.1177\/21582440231153857.<\/li>\n
  4. [4] A. Bonfanti, V. Vigolo, V. Vannucci, and F. Brunetti, (2023) \u201cCreating memorable shopping experiences to meet phygital customers\u2019 needs: evidence from sporting goods stores\u201d International Journal of Retail and Distribution Management 51(13): 81\u2013100. DOI: 10.1108\/IJRDM-12-2021-0588.<\/li>\n
  5. [5] S. e. a. Pardini, (2022) \u201cThe Role of Personalization in the User Experience, Preferences and Engagement with Virtual Reality Environments for Relaxation\u201d International Journal of Environmental Research and Public Health 19(12): 7237. DOI: 10.3390\/ijerph19127237.<\/li>\n
  6. [6] J. Yang, C. Cao, C. Ye, and Y. Shi, (2022) \u201cEffects of Interface Design and Live Atmosphere on Consumers\u2019 Impulse-Buying Behaviour from the Perspective of Human\u2013Computer Interaction\u201d Sustainability 14(12): 7110. DOI: 10.3390\/su14127110.<\/li>\n
  7. [7] A. Braca and D. Dondio, (2023) \u201cDeveloping persuasive systems for marketing: the interplay of persuasion techniques, customer traits and persuasive message design\u201d Italian Journal of Marketing 2023(3): 369\u2013412. DOI: 10.1007\/s43039-023-00077-0.<\/li>\n
  8. [8] N. e. a. Etxaburu, (2024) \u201cBuying-shopping disorder, impulsivity, emotional dependence and attachment in adolescents\u201d Current Psychology 43(2): 1507\u20131518. DOI: 10.1007\/s12144-023-04425-3.<\/li>\n
  9. [9] J. Nyrhinen, K. Lonka, A. Sirola, M. Ranta, and T.-A. Wilska, (2023) \u201cYoung adults\u2019 online shopping addiction: The role of self-regulation and smartphone use\u201d International Journal of Consumer Studies 47(5): 1871\u20131884. DOI: 10.1111\/ijcs.12961.<\/li>\n
  10. [10] J. e. a. David, (2024) \u201cEmotional difficulties mediate the impact of adverse childhood experiences on compulsive buying-shopping problems\u201d Journal of Behavioral Addictions 13(4): 1064\u20131073. DOI: 10.1556\/2006.2024.00056.<\/li>\n
  11. [11] D. Vrtana and A. Krizanova, (2023) \u201cThe Power of Emotional Advertising Appeals: Examining Their Influence on Consumer Purchasing Behavior and Brand\u2013Customer Relationship\u201d Sustainability 15(18): 13337. DOI: 10.3390\/su151813337.<\/li>\n
  12. [12] B. Bharadwaj, S. Nayak, and P. K. Panigrahi, (2025) \u201cSentiment analysis for identifying depression through social media texts using machine learning\u201d Big Data Computing and Visualization 5(2): 102\u2013118. DOI: 10.22105\/bdcv.2025.499270.1239.<\/li>\n
  13. [13] J. Sathya, M. Babu, J. Gayathri, G. Indhumathi, Z. A. Samad, and E. Anandhi, (2023) \u201cCompulsive buying behavior and online shopping addiction of women\u201d Information Science Letters 12(5): 1641\u20131650. DOI: 10.17632\/m4bp9p2n8j.1.<\/li>\n
  14. [14] S.-V. Oprea and A. B\u00e2ra, (2025) \u201cExtracting Emotions from Customer Reviews Using Text Mining, Large Language Models and Fine-Tuning Strategies\u201d Journal of Theoretical and Applied Electronic Commerce Research 20(3): 221. DOI: 10.3390\/jtaer20030221.<\/li>\n
  15. [15] P. Patel et al., (2025) \u201cApplication of supervised machine learning models in human emotion classification using Tsallis entropy as a feature\u201d Journal of Big Data 12(1): 126. DOI: 10.1186\/s40537-025-01177-8.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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