An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis
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. 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 |
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Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences Faculty of Science > Tyndall Centre for Climatic Change |
Depositing User: | LivePure Connector |
Date Deposited: | 01 Dec 2020 00:27 |
Last Modified: | 18 Jan 2021 00:35 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/77840 |
DOI: | 10.1007/978-3-030-61705-9_14 |
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