Abstract
Music plays an important role in our daily life. It can have a powerful effect on our emotions, mental health, and even the community we live in. Although numerous studies have been conducted to prove the great impact music has on humans, few investigations place an emphasis on the exploration of the relationship between music and listener’s sentiment. To this end, we first examined three song demographics: Beats Per Minute, Key, and Length, and six song metrics: Danceability, Energy, Speechiness, Acousticness, Liveness, and Valence of popular songs, and then conducted an empirical study to examine the potential correlation between song demographics/metrics and the sentiment expressed as in written text (such as social media). To accomplish this, we scraped around 20 million tweets referencing the most popular songs from 2018 to 2022 as shown on Spotify’s Top Global chart, as well as the immediate surrounding tweets, and performed a double sentiment analysis on the data. Our study concludes that there exists a significant correlation between all the pairs of song metrics. From the sentiment analysis of tweets, our results indicate that there may not be a significant correlation between the sentiment expressed in tweets of a song’s listeners and the song itself. Our study provides empirical evidence for a deeper understanding of popular songs using data mining techniques.
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Stefanzick, J., Zhao, X. (2023). Popular Songs: The Sentiment Surrounding the Conversation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_25
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DOI: https://doi.org/10.1007/978-3-031-46661-8_25
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