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

^*Corresponding author for this work

Field Service Divisional Office

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

2 Citations (Scopus)

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 - 15th International Conference, HAIS 2020, Proceedings
Editors	Enrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	160-171
Number of pages	12
Volume	12344
ISBN (Electronic)	9783030617059
ISBN (Print)	9783030617042
DOIs	https://doi.org/10.1007/978-3-030-61705-9_14
Publication status	E-pub ahead of print - 4 Nov 2020
Event	15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 - Gijón, Spain Duration: 11 Nov 2020 → 13 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

Conference

Conference	15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020
Country/Territory	Spain
City	Gijón
Period	11/11/20 → 13/11/20

Bibliographical note

Funding Information: Supported by Public Health England.

Open Access: No Open Access licence.

Publisher Copyright: © 2020, Springer Nature Switzerland AG.

Citation: Edo-Osagie O., Iglesia B.D.L., Lake I., Edeghere O. (2020) An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In: de la Cal E.A., Villar Flecha J.R., Quintián H., Corchado E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science, vol 12344. Springer, Cham.

DOI: https://doi.org/10.1007/978-3-030-61705-9_14

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

Cite this

Edo-Osagie, O., Iglesia, B. D. L., 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 - 15th International Conference, HAIS 2020, Proceedings (Vol. 12344, pp. 160-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12344 LNAI). Springer Science and Business Media Deutschland GmbH. Advance online publication. https://doi.org/10.1007/978-3-030-61705-9_14

Edo-Osagie, Oduwa ; Iglesia, Beatriz De La ; Lake, Iain et al. / An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings. editor / Enrique Antonio de la Cal ; José Ramón Villar Flecha ; Héctor Quintián ; Emilio Corchado. Vol. 12344 Springer Science and Business Media Deutschland GmbH, 2020. pp. 160-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6d45a78a53cf444ea88a69c32f379bd1,

title = "An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis",

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.",

keywords = "Evolutionary computing, Social media sensing, Syndromic surveillance, Twitter",

author = "Oduwa Edo-Osagie and Iglesia, \{Beatriz De La\} and Iain Lake and Obaghe Edeghere",

note = "Funding Information: Supported by Public Health England. Open Access: No Open Access licence. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Citation: Edo-Osagie O., Iglesia B.D.L., Lake I., Edeghere O. (2020) An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In: de la Cal E.A., Villar Flecha J.R., Quinti{\'a}n H., Corchado E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science, vol 12344. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-61705-9\_14 ; 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 ; Conference date: 11-11-2020 Through 13-11-2020",

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editor = "\{de la Cal\}, \{Enrique Antonio\} and \{Villar Flecha\}, \{Jos{\'e} Ram{\'o}n\} and H{\'e}ctor Quinti{\'a}n and Emilio Corchado",

booktitle = "Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings",

}

Edo-Osagie, O, Iglesia, BDL, 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 - 15th International Conference, HAIS 2020, Proceedings. vol. 12344, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12344 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 160-171, 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020, Gijón, Spain, 11/11/20. 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; Iglesia, Beatriz De La; Lake, Iain et al.
Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings. ed. / Enrique Antonio de la Cal; José Ramón Villar Flecha; Héctor Quintián; Emilio Corchado. Vol. 12344 Springer Science and Business Media Deutschland GmbH, 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 › peer-review

TY - GEN

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AU - Edo-Osagie, Oduwa

AU - Iglesia, Beatriz De La

AU - Lake, Iain

AU - Edeghere, Obaghe

N1 - Funding Information: Supported by Public Health England. Open Access: No Open Access licence. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Citation: Edo-Osagie O., Iglesia B.D.L., Lake I., Edeghere O. (2020) An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In: de la Cal E.A., Villar Flecha J.R., Quintián H., Corchado E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science, vol 12344. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-61705-9_14

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

UR - https://www.scopus.com/pages/publications/85097051058

U2 - 10.1007/978-3-030-61705-9_14

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

M3 - Conference contribution

AN - SCOPUS:85097051058

SN - 9783030617042

VL - 12344

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

SP - 160

EP - 171

BT - Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings

A2 - de la Cal, Enrique Antonio

A2 - Villar Flecha, José Ramón

A2 - Quintián, Héctor

A2 - Corchado, Emilio

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020

Y2 - 11 November 2020 through 13 November 2020

ER -

Edo-Osagie O, Iglesia BDL, 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 - 15th International Conference, HAIS 2020, Proceedings. Vol. 12344. Springer Science and Business Media Deutschland GmbH. 2020. p. 160-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2020 Nov 4. doi: 10.1007/978-3-030-61705-9_14

An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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