Performance Analysis of Support Vector Machine with Hyperparameter Tuning for Engagement Rate Classification of TikTok Digital Marketing Content
Abstract
The effectiveness of TikTok digital marketing content often varies, creating challenges for marketers in achieving consistent audience engagement. Differences in posting time, upload frequency, video length, and music selection make it difficult to distinguish engagement rate (ER) levels accurately. This paper investigates key factors affecting engagement and proposes a classification framework to separate high- and low-engagement TikTok content. A machine learning approach using the Support Vector Machine (SVM) algorithm is applied to a dataset of TikTok videos collected between 2023 and 2025. From an initial dataset of 2,992 videos, 1,638 representative samples were retained after data cleaning and creator-level filtering. The research process involves feature engineering, engagement-based labelling with a 5% ER threshold, data normalisation, and dataset partitioning with an 80:20 training–testing split. The baseline SVM model achieved an accuracy of 70.43%, indicating limited ability to distinguish low-engagement content. After systematic hyperparameter tuning, the optimised linear SVM model demonstrated improved performance, achieving an accuracy of 88.41% with an optimal regularisation parameter (C = 100) and more balanced classification results. Model interpretation indicates that video duration, temporal attributes, and audio characteristics play important roles in separating engagement levels. The proposed framework is intended for post-hoc engagement classification rather than engagement prediction, providing interpretable insights to support TikTok digital marketing strategy optimization.
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