Artificial Intelligence-Based Consumer Emotion Modeling: Quantifying the Interplay between Self-Depletion, Emotional Compensation and Shopping Addiction

Haiyang  wu

doi:10.6180/jase.202609_32.006

Artificial Intelligence-Based Consumer Emotion Modeling: Quantifying the Interplay between Self-Depletion, Emotional Compensation and Shopping Addiction

Computer Science and Information Engineering

Haiyang wuThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Digital Commerce and TradeChangzhou Vocational Institute of Textile and GarmentChangzhou 213164, Jiangsu, China.

Received: December 13, 2025
Accepted: March 22, 2026
Publication Date: April 12, 2026

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

Download Citation: ||https://doi.org/10.6180/jase.202609_32.006

This paper proposes an AI framework analyzing psychological factors in online shopping addiction and com pulsive purchasing behavior. This study utilizes an openly available Mendeley dataset containing behavioural, demographic, and emotional indicators of women’s 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.

Keywords: Consumer Emotion Modelling; Dense Emotional Layer (DEL); GRU-Transformer Hybrid Model; Online Shopping Addiction

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