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

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
ISBN (Print)9783030617042
DOIs
Publication statusPublished - 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.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

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

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