Identifying Key Mental Health Topic on Youtube Comments using Non-negative Matrix Factorization
This study addresses the challenge of identifying key mental health topics within YouTube comments, a resource-rich yet underutilized data source for mental health discourse analysis. The research employed Non-Negative Matrix Factorization (NMF) for topic modeling, coupled with sentiment analysis to...
| 出版年: | 2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 |
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| 第一著者: | |
| フォーマット: | Conference paper |
| 言語: | English |
| 出版事項: |
Institute of Electrical and Electronics Engineers Inc.
2024
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219571902&doi=10.1109%2fSCOReD64708.2024.10872731&partnerID=40&md5=420b93368b2c5f335254697c957a1142 |