An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis
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ToolsEdo-Osagie, Oduwa, Iglesia, Beatriz De La, Lake, Iain and Edeghere, Obaghe (2020) An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis. In: Hybrid Artificial Intelligent Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing AG, Cham, pp. 160-171. ISBN 978-3-030-61705-9
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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.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | evolutionary computing,social media sensing,syndromic surveillance,twitter,theoretical computer science,computer science(all),sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2600/2614 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences University of East Anglia > Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 01 Dec 2020 00:27 |
| Last Modified: | 12 May 2022 00:04 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/77840 |
| DOI: | 10.1007/978-3-030-61705-9_14 |
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