An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

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Edo-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) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science > School of Environmental Sciences
Faculty of Science > Tyndall Centre for Climatic Change
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Depositing User: LivePure Connector
Date Deposited: 01 Dec 2020 00:27
Last Modified: 11 Apr 2021 23:57
URI: https://ueaeprints.uea.ac.uk/id/eprint/77840
DOI: 10.1007/978-3-030-61705-9_14

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