System development for enhancing social media advertisement engagement through XLNet-based personality classification

Abstract

This research focuses on addressing the challenge of implementing personalized advertisements in the retail industry, where existing methods often face complexities that hinder their swift and large-scale adoption. The primary objective of this study was to develop a scalable and efficient social media advertisement personalization system by employing advanced personality classification techniques. The system utilizes the myPersonality dataset, grounded in the Big 5 OCEAN traits theory, to accurately classify user personalities. By integrating the XLNet model, optimized for personality classification, the system achieves a classification accuracy of 97.47 %, with precision, recall, and F1-Score values of 0.95, 0.94, and 0.94, respectively. The findings demonstrate that personalized advertisements, driven by accurately classified personality traits, significantly enhance user interaction rates, showing a 24 % improvement over generalized advertisements. This improvement in engagement suggests that the system can effectively personalize advertisements to resonate more deeply with users, fostering stronger connections between users and the advertised content. The proposed system's high accuracy and improved interaction rates make it a valuable addition to current marketing strategies, enhancing both engagement and conversion rates. This innovative approach has the potential to transform personalized advertising, making it more effective and widely adoptable within the marketing sector