2025/08/29 by Yunwoo Kim, Kim, Yunwoo, Hwang, Junhyuk
Computer Science · Psychology · #Emotion and Mood Recognition #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health via Writing #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.2508.21650
openalex publication_date 2025/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets. Performance is evaluated with both a custom order of magnitude accuracy and standard regression metrics, including the coefficient of determination (R2). Results show that emotional and temporal metadata, together with existing view counts, predict future engagement effectively. The model attains R2 = 0.98 for likes but only R2 = 0.41 for comments. This gap indicates that likes are largely driven by readily captured affective and exposure signals, whereas comments depend on additional factors not represented in the current feature set.