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 proceedingConference contribution

2 Citations (Scopus)
13 Downloads (Pure)


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
EditorsEnrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado
Place of PublicationCham
Number of pages12
ISBN (Print)978-3-030-61705-9
Publication statusPublished - 4 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


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

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