Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic
In this paper, we extract quotations from Al Jazeera’s news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to se...
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| Natura: | Conference paper |
| Lingua: | English |
| Pubblicazione: |
2023
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| Accesso online: | http://scholar.dlu.edu.vn/handle/123456789/2006 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Riassunto: | In this paper, we extract quotations from Al Jazeera’s news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to see how coronavirus impacts human beings. By combining noun phrases as inputs before the training and Cv measure for coherence values, we obtain an average coherence value of 0.66 with a least average number of topics of 24.8. The result covers some of the top issues that our world has been facing against the COVID-19 pandemic. |
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