Image-Based User Feature Classification on Social Media Using Machine Learning
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Abstract
This study presents a robust machine learning framework for image-based user feature classification on social media, with a particular emphasis on applications in e-commerce environments. To address the critical challenge of limited labeled data within large-scale and unstructured image repositories, the proposed approach integrates semi-supervised learning techniques to enhance model generalization and data utilization efficiency. Three widely adopted machine learning algorithms, Decision Tree, Random Forest, and Support Vector Machine (SVM), were systematically evaluated using K-fold cross-validation and standard performance metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest model outperforms other approaches, achieving an accuracy of 0.9468 and an F1-score of 0.9456, while maintaining strong computational efficiency and robustness. These findings underscore the effectiveness of ensemble learning methods in handling high-dimensional, imbalanced datasets. The proposed framework offers significant practical implications for user profiling, personalized recommendation systems, and targeted marketing strategies in modern digital ecosystems
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The data used in this study are not publicly available due to privacy and platform restrictions but may be available from the corresponding author upon reasonable request
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