An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

Oduwa Edo-Osagie; Beatriz De La Iglesia; Iain Lake; Obaghe Edeghere

doi:10.1007/978-3-030-61705-9_14

An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

Oduwa Edo-Osagie, Beatriz De La Iglesia, Iain Lake, Obaghe Edeghere

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

2 Citations (Scopus)

28 Downloads (Pure)

Abstract

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems
Editors	Enrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado
Place of Publication	Cham
Publisher	Springer
Pages	160-171
Number of pages	12
ISBN (Print)	978-3-030-61705-9
DOIs	https://doi.org/10.1007/978-3-030-61705-9_14
Publication status	Published - 4 Nov 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12344 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Evolutionary computing
Social media sensing
Syndromic surveillance
Twitter

Access to Document

10.1007/978-3-030-61705-9_14

Accepted_ManuscriptAccepted author manuscript, 351 KB

Cite this

Edo-Osagie, O., De La Iglesia, B., Lake, I., & Edeghere, O. (2020). An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In E. A. de la Cal, J. R. Villar Flecha, H. Quintián, & E. Corchado (Eds.), Hybrid Artificial Intelligent Systems (pp. 160-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12344 LNAI). Springer. https://doi.org/10.1007/978-3-030-61705-9_14

Edo-Osagie, Oduwa ; De La Iglesia, Beatriz ; Lake, Iain et al. / An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. Hybrid Artificial Intelligent Systems. editor / Enrique Antonio de la Cal ; José Ramón Villar Flecha ; Héctor Quintián ; Emilio Corchado. Cham : Springer, 2020. pp. 160-171 (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 propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.",

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Edo-Osagie, O, De La Iglesia, B , Lake, I & Edeghere, O 2020, An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. in EA de la Cal, JR Villar Flecha, H Quintián & E Corchado (eds), Hybrid Artificial Intelligent Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12344 LNAI, Springer, Cham, pp. 160-171. https://doi.org/10.1007/978-3-030-61705-9_14

An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. / Edo-Osagie, Oduwa; De La Iglesia, Beatriz ; Lake, Iain et al.
Hybrid Artificial Intelligent Systems. ed. / Enrique Antonio de la Cal; José Ramón Villar Flecha; Héctor Quintián; Emilio Corchado. Cham: Springer, 2020. p. 160-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12344 LNAI).

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

TY - GEN

T1 - An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

AU - Edo-Osagie, Oduwa

AU - De La Iglesia, Beatriz

AU - Lake, Iain

AU - Edeghere, Obaghe

PY - 2020/11/4

Y1 - 2020/11/4

N2 - In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.

AB - In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.

KW - Evolutionary computing

KW - Social media sensing

KW - Syndromic surveillance

KW - Twitter

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SN - 978-3-030-61705-9

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

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BT - Hybrid Artificial Intelligent Systems

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Edo-Osagie O, De La Iglesia B , Lake I, Edeghere O. An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In de la Cal EA, Villar Flecha JR, Quintián H, Corchado E, editors, Hybrid Artificial Intelligent Systems. Cham: Springer. 2020. p. 160-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-61705-9_14

An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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Profiles

Beatriz De La Iglesia

Iain Lake

Cite this