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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings
EditorsEnrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado
PublisherSpringer Science and Business Media Deutschland GmbH
Pages160-171
Number of pages12
Volume12344
ISBN (Electronic)9783030617059
ISBN (Print)9783030617042
DOIs
Publication statusE-pub ahead of print - 4 Nov 2020
Event15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 - Gijón, Spain
Duration: 11 Nov 202013 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12344 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020
Country/TerritorySpain
CityGijón
Period11/11/2013/11/20

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

Keywords

  • Evolutionary computing
  • Social media sensing
  • Syndromic surveillance
  • Twitter

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