TY - GEN
T1 - Lyrics-based Mood Detection in Music using Text Mining Techniques
AU - Punyashree, S.
AU - Harshitha, G. M.
AU - Rashmi,
AU - Murthy, Anantha
AU - Shetty, Keerthi
AU - Ramyashree, null
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper classifies songs based on lyrical content for the growing demand of better user experiences and personal music recommendations. The main idea is to come up with a classifier that could determine whether a song is happy, sad, calm, or energetic based on the lyrics. It uses in-depth text analysis over data retrieved from kaggle(Spotify) to see word distributions which differ across categories of emotions. Achieving meaningful text representation, feature selection uses the Bag of Words (BOW) model, along with Part-of-Speech (POS) tagging and stemming through WordNet. The classification model built with Random Forest and XGBoost uses hyperparameter tuning through grid search to improve model performance. The outcome of this research demonstrates the effective application of text mining techniques in Python to analyze, categorize, and predict the emotion of music, thus offering a more personalized music experience for users.
AB - This paper classifies songs based on lyrical content for the growing demand of better user experiences and personal music recommendations. The main idea is to come up with a classifier that could determine whether a song is happy, sad, calm, or energetic based on the lyrics. It uses in-depth text analysis over data retrieved from kaggle(Spotify) to see word distributions which differ across categories of emotions. Achieving meaningful text representation, feature selection uses the Bag of Words (BOW) model, along with Part-of-Speech (POS) tagging and stemming through WordNet. The classification model built with Random Forest and XGBoost uses hyperparameter tuning through grid search to improve model performance. The outcome of this research demonstrates the effective application of text mining techniques in Python to analyze, categorize, and predict the emotion of music, thus offering a more personalized music experience for users.
UR - https://www.scopus.com/pages/publications/105001666145
UR - https://www.scopus.com/pages/publications/105001666145#tab=citedBy
U2 - 10.1109/IDCIOT64235.2025.10914753
DO - 10.1109/IDCIOT64235.2025.10914753
M3 - Conference contribution
AN - SCOPUS:105001666145
T3 - 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025
SP - 477
EP - 486
BT - 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025
Y2 - 5 February 2025 through 7 February 2025
ER -