Exploring the Emotional Landscape of Music: An Analysis of Valence Trends and Genre Variations in Spotify Music Data
This provides incremental insights into music-emotion relationships for musicologists and streaming platforms, using existing methods on new data.
This paper analyzed musical emotions using Spotify data to identify patterns in valence scores across genres and time, finding that valence distribution has shifted over the years and that certain genres exhibit distinct emotional characteristics.
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.