Music Recommendation Systum Based on Emotion
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1752356
ABSTRACT
With the onset of lockdown in the COVID-19 scenario, people were forced to confine themselves within the four walls of their rooms which in the meantime invited mood disorders like depression, anxiety etc. Music has proven to be a potential empathetic companion in this tough time for all. The proposed emotion-based music recommendation system uses aser emotion as an input to recommend songs that are-ascertained using faciai expression or using direct inputs from the user. The model uses a Random Forest classifier and XGBoost algorithm to identify the song's emotion considering various features like instruineiitainess, energy, acoustics, liveness, etc, and lyrical similarity among songs with the help of Term-Frequency times Inverse Document-Frequency (TF-IBF). The results of comprehensive experiments on reai data confirm the accuracy of the proposed emotion classification system that can be integrated into any recommendation engine. © 2021 IEEE.
Emotion classification; Lyrics classification; Machine Learning; Random Forest; Recommendation System; XGBoost; Decision trees; Music; Text processing; Lyric classification; Machine-learning; Model use; Mood disorders; Music recommendation; Music Recommendation System; Random forests; System use; Recommender systems
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021
Year:
2021
Document Type:
Article
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