Sarsam, S.M., Alzahrani, A.I. and Al-Samarraie, H. orcid.org/0000-0002-9861-8989 (2024) What topics and emotions expressed by glaucoma patients? A sentiment analysis perspective. Social Network Analysis and Mining, 14 (1). 155. ISSN 1869-5450
Abstract
The recognition of eye disorders has the potential to reduce blindness in people. The need for a procedural method is important to boost the overall recognition process. Although the identification of certain disease symptoms is crucial to an early diagnosis, this study proposed a procedural mechanism to predict eye diseases on the Twitter platform using users’ sentiments embedded in their social media data. Glaucoma was investigated as one example of various eye diseases. Themes related to glaucoma were extracted using Latent Dirichlet Allocation. Subsequently, association rules mining was employed to identify disease-related symptoms within each theme. Our results showed that certain emotions, such as fear and sadness emotions, were highly associated with glaucoma messages. The findings revealed that emotion-related features have a significant impact on improving the prediction process of glaucoma in patients. As a result, this study proposes a low-cost procedural mechanism for the early-stage detection of eye disorders using microblogs data. The proposed approach can advance current efforts toward developing clinical decision support systems capable of detecting diseases online.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s13278-024-01309-7 . |
Keywords: | Social media mining, Glaucoma recognition, Machine learning, Decision-making |
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: | 13 Nov 2024 15:47 |
Last Modified: | 13 Nov 2024 15:47 |
Published Version: | https://link.springer.com/article/10.1007/s13278-0... |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s13278-024-01309-7 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219582 |
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