Unlocking Customer Insights: A Hybrid SVM - GPT Transformer Model for User Engagement

Authors

  • A. Raja Research Scholar, Periyar University, Salem, Tamil Nadu, India.
  • S. Prema HOD Department of Computer Application, Arulmigu Arthanareeswarar Arts and Science College, Tiruchengodu, Namakkal, Tamil Nadu, India

Keywords:

Clusters, Generative pre-trained transformers, Distance, Support Vectors, Temporal Factors and social media

Abstract

A key component of today's informal advertising is online users have a direct impact on a business's reputation and profitability influencing consumers' purchasing preferences and buying decisions. Based on the customer segmentation provides more effective business reforms in online media. This research paper proposes a hybrid approach to concentrate the textual and sentimental activities of the consumer based on the temporal constrained factors. The data is classified based on ordinal and nominal values by forming clusters using the hybrid support vector regression (H-SVR) classifier in conjunction with the recommended pre-trained transformers and euclidean distance. Using random forest, k-nearest neighbor, and linear SVM, the suggested learning classifier's performance is compared. When compared to other classifiers, the suggested algorithm achieved the best accuracy.

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Published

05.12.2023

How to Cite

Raja, A. ., & Prema, S. . (2023). Unlocking Customer Insights: A Hybrid SVM - GPT Transformer Model for User Engagement. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 441–448. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4092

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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