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Edo-Osagie, Oduwa, De La Iglesia, Beatriz 
ORCID: https://orcid.org/0000-0003-2675-5826, Lake, Iain ORCID: https://orcid.org/0000-0003-4407-5357 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, 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.
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