This paper conducts an intricate analysis of musical emotions and trends using Spotify music data, encompassing audio features and valence scores extracted through the Spotipi API. Employing regression modeling, temporal analysis, mood transitions, and genre investigation, the study uncovers patterns within music-emotion relationships. Regression models linear, support vector, random forest, and ridge, are employed to predict valence scores. Temporal analysis reveals shifts in valence distribution over time, while mood transition exploration illuminates emotional dynamics within playlists. The research contributes to nuanced insights into music's emotional fabric, enhancing comprehension of the interplay between music and emotions through years.

翻译：本文运用Spotify音乐数据，通过Spotipi API提取音频特征与效价分数，对音乐情感及趋势进行了深入分析。研究采用回归建模、时间序列分析、情绪转换及流派探究方法，揭示了音乐-情感关系中的模式。线性回归、支持向量机、随机森林及岭回归等模型被用于预测效价分数。时间分析揭示了效价分布随时间的演变趋势，而情绪转换探索阐明了播放列表中的情感动态。本研究为理解音乐的感性结构提供了细致洞见，增强了多年来对音乐与情感相互作用的认识。