| Título |
Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
19th Mexican International Conference on Artificial Intelligence, MICAI 2020 |
| Resumen |
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. © 2020, Springer Nature Switzerland AG. |
| Observaciones |
DOI 10.1007/978-3-030-60887-3_5
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12469 |
| Lugar |
Ciudad de México |
| País |
Mexico |
| No. de páginas |
51-64 |
| Vol. / Cap. |
12469 LNAI |
| Inicio |
2020-10-12 |
| Fin |
2020-10-17 |
| ISBN/ISSN |
9783030608866 |
