Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

Thang Hoang Ta; Abu Bakar Siddiqur Rahman; Grigori Sidorov; Alexander Gelbukh

doi:10.1007/978-3-030-60887-3_5

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

Thang Hoang Ta, Abu Bakar Siddiqur Rahman, Grigori Sidorov, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 C_v 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.

Original language	English
Title of host publication	Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
Editors	Lourdes Martínez-Villaseñor, Hiram Ponce, Oscar Herrera-Alcántara, Félix A. Castro-Espinoza
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	51-64
Number of pages	14
ISBN (Print)	9783030608866
DOIs	https://doi.org/10.1007/978-3-030-60887-3_5
State	Published - 2020
Event	19th Mexican International Conference on Artificial Intelligence, MICAI 2020 - Mexico City, Mexico Duration: 12 Oct 2020 → 17 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12469 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Country/Territory	Mexico
City	Mexico City
Period	12/10/20 → 17/10/20

Keywords

COVID-19
Latent Dirichlet Allocation
Quotation mining
Topic model

Access to Document

10.1007/978-3-030-60887-3_5

Cite this

Ta, T. H., Rahman, A. B. S., Sidorov, G., & Gelbukh, A. (2020). Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic. In L. Martínez-Villaseñor, H. Ponce, O. Herrera-Alcántara, & F. A. Castro-Espinoza (Eds.), Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings (pp. 51-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12469 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60887-3_5

Ta, Thang Hoang ; Rahman, Abu Bakar Siddiqur ; Sidorov, Grigori et al. / Mining Hidden Topics from Newspaper Quotations : The COVID-19 Pandemic. Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. editor / Lourdes Martínez-Villaseñor ; Hiram Ponce ; Oscar Herrera-Alcántara ; Félix A. Castro-Espinoza. Springer Science and Business Media Deutschland GmbH, 2020. pp. 51-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we extract quotations from Al Jazeera{\textquoteright}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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Ta, TH, Rahman, ABS, Sidorov, G & Gelbukh, A 2020, Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic. in L Martínez-Villaseñor, H Ponce, O Herrera-Alcántara & FA Castro-Espinoza (eds), Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12469 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 51-64, 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, 12/10/20. https://doi.org/10.1007/978-3-030-60887-3_5

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic. / Ta, Thang Hoang; Rahman, Abu Bakar Siddiqur; Sidorov, Grigori et al.
Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. ed. / Lourdes Martínez-Villaseñor; Hiram Ponce; Oscar Herrera-Alcántara; Félix A. Castro-Espinoza. Springer Science and Business Media Deutschland GmbH, 2020. p. 51-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12469 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mining Hidden Topics from Newspaper Quotations

T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020

AU - Ta, Thang Hoang

AU - Rahman, Abu Bakar Siddiqur

AU - Sidorov, Grigori

AU - Gelbukh, Alexander

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - COVID-19

KW - Latent Dirichlet Allocation

KW - Quotation mining

KW - Topic model

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings

A2 - Martínez-Villaseñor, Lourdes

A2 - Ponce, Hiram

A2 - Herrera-Alcántara, Oscar

A2 - Castro-Espinoza, Félix A.

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 12 October 2020 through 17 October 2020

ER -

Ta TH, Rahman ABS, Sidorov G , Gelbukh A. Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic. In Martínez-Villaseñor L, Ponce H, Herrera-Alcántara O, Castro-Espinoza FA, editors, Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 51-64. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60887-3_5

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

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