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

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’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.