Sarsam, SM and Al-Samarraie, H orcid.org/0000-0002-9861-8989 (2022) A lexicon-based method for detecting eye diseases on microblogs. Applied Artificial Intelligence, 36 (1). 1993003. pp. 1-12. ISSN 0883-9514
Abstract
This paper explored the feasibility of detecting eye diseases on microblogs. A lexicon-based approach was developed to provide an early recognition of common eye disease from social media platforms. The data were obtained using Twitter free streaming Application Programming Interface (API). A cluster analysis was applied to extract instances that share similar characteristics. We extracted three types of emotions (positive, negative, and neutral) from users’ messages (tweets) using SentiStrength. A time-series method was used to determine the applicability of predicting emotional changes over a period of seven months. The relevant disease symptoms were extracted using Apriori algorithm with prediction accuracy of 98.89%. This study offers a timely and effective method that can be implemented to help healthcare decision makers and researchers reduce the spread of eye diseases in a population specific manner.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Taylor & Francis. This is an author produced version of an article published in Applied Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Oct 2021 14:07 |
Last Modified: | 21 Oct 2022 00:13 |
Status: | Published |
Publisher: | Taylor and Francis |
Identification Number: | 10.1080/08839514.2021.1993003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179650 |