Abstract
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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Acknowledgements
The work was done with support of the Government of Mexico via CONACYT, SNI, CONACYT grant A1-S-47854, and grants SIP 20200797, SIP 20200859 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico.
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Ta, T.H., Rahman, A.B.S., Sidorov, G., Gelbukh, A. (2020). Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_5
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