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.

Authors: Shashwat Mookherjee, Shruti Dutta

Published in: International Conference for Internet Technology and Secured Transactions (ICITST-2023)

  • Date of Conference: 13-15 November 2023
  • DOI: 10.20533/ICITST.2023.0013
  • ISBN: 978-1-913572-63-1
  • Conference Location: St Anne’s College, Oxford University, UK