<![CDATA[Social media platforms, particularly TikTok, have become important sources of consumer-generated information that influence product awareness and purchasing decisions in the skincare industry. However, audience engagement metrics alone cannot fully explain audience perception because highly engaging content does not always receive positive responses from users. This study aims to propose and evaluate an Engagement–Sentiment Gap framework that integrates audience interaction and audience perception to provide a more comprehensive evaluation of TikTok skincare content than conventional sentiment analysis or engagement-based approaches. A dataset comprising 20,688 comments and metadata from 603 TikTok videos across ten skincare-related topics was collected through web scraping. After text preprocessing, sentiment classification was performed using a fine-tuned IndoBERT model, followed by video-level sentiment aggregation, engagement score calculation, Engagement–Sentiment Gap categorization, and engagement-level prediction using Random Forest and XGBoost. The results showed that neutral sentiment dominated the discussions (72.5%), while audience sentiment exhibited a statistically significant but weak positive correlation with engagement (r = 0.2283, p < 0.001). Random Forest achieved the best predictive performance, with 65.27% Balanced Accuracy and 68.74% ROC-AUC, while topic was identified as the most influential predictive feature. These findings demonstrate that integrating sentiment and engagement within the proposed framework provides richer insights into audience behavior and content performance than evaluating engagement or sentiment independently, offering a practical analytical approach for social media content evaluation.]]>
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