Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17330
ชื่อเรื่อง: Mood classification from Song Lyric using Machine Learning
ผู้แต่ง: Siriket K.
Sa-Ing V.
Khonthapagdee S.
Keywords: Adaptive boosting
Decision trees
Logistic regression
Natural language processing systems
Statistics
Boosting algorithm
Grid search
Latent dirichlet allocations
NAtural language processing
Special characters
Term Frequency
Machine learning
วันที่เผยแพร่: 2021
บทคัดย่อ: Nowadays, many people change the way they listen to music by listening to the mood of the songs in the tracks. This research is interested in analyzing song extraction using natural language processing to acquire mood information. Lyrics are valuable for categorizing music. First, removing special characters and using Term-frequency/inverse-document frequency (TFIDF) and then Latent Dirichlet Allocation (LDA) are used to connect words to mood classes. We perform a lyric-based mood classification on local machine learning classifiers such as Random forest, Decision tree, Naïve Bayes, Logistic Regression, AdaBoost and XGBoost. Using grid search for tuning the best parameter yield the results XGBoost shows the highest accuracy. It can prove that boosting algorithms have better performance than local machine learning in this research. © 2021 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/17330
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107800707&doi=10.1109%2fiEECON51072.2021.9440333&partnerID=40&md5=bc7c300d6ce77f6aa2d6d76d3375a6f7
Appears in Collections:Scopus 1983-2021

Files in This Item:
There are no files associated with this item.


Items in SWU repository are protected by copyright, with all rights reserved, unless otherwise indicated.