Research on E-Commerce User Profiling Based on the Integration of Sentiment Analysis and Big Data and Emotion-Driven Consumer Behavior

Linyuan Gao

doi:10.71222/ar03cm65

Authors

Linyuan Gao Business School, Xinyang Vocational College of Art, Xinyang, China Author

DOI:

https://doi.org/10.71222/ar03cm65

Keywords:

sentiment analysis, e-commerce, user profiling, consumer behavior, big data, decision making

Abstract

With the rapid advancement of big data and artificial intelligence technologies, sentiment analysis has emerged as an indispensable and highly effective tool for understanding complex user emotions and significantly enhancing e-commerce personalization. Traditional user profiling methodologies predominantly focus on demographic and historical transaction data, frequently overlooking the critical emotional dimension of consumers. This fundamental limitation restricts the accuracy of behavior prediction and diminishes the efficacy of decision-making support systems. To address this gap, this study proposes a novel, comprehensive approach to e-commerce user profiling by seamlessly integrating advanced sentiment analysis with robust big data techniques to construct multi-dimensional, emotion-aware consumer profiles. The research develops an innovative framework that systematically incorporates emotional factors into dynamic user models, rigorously analyzes their direct influence on purchasing behavior, and explores how fluctuating emotional states drive diverse consumption patterns across various product categories. Through extensive multi-source data collection—encompassing social media interactions, customer reviews, and real-time browsing behaviors—coupled with state-of-the-art natural language processing algorithms and established consumer psychology models, the research reveals profound and statistically significant correlations between specific user emotions and subsequent purchase decisions. The empirical findings provide a robust theoretical basis for refining targeted marketing strategies, improving user-centric product design, and optimizing personalized recommendation systems to achieve higher conversion rates. Ultimately, this research not only contributes to the broader academic understanding of emotion-driven consumption but also offers actionable, practical guidance for the development of intelligent, emotionally responsive e-commerce service ecosystems.

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