A lexicon-based method for detecting eye diseases on microblogs
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 cluste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1993003 |
| _version_ | 1797641082493730816 |
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| author | Samer Muthana Sarsam Hosam Al-Samarraie |
| author_facet | Samer Muthana Sarsam Hosam Al-Samarraie |
| author_sort | Samer Muthana Sarsam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T13:40:25Z |
| format | Article |
| id | doaj.art-7c696535e7b44b63a289ce02875a5d17 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:25Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-7c696535e7b44b63a289ce02875a5d172023-11-02T13:36:37ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2021.19930031993003A lexicon-based method for detecting eye diseases on microblogsSamer Muthana Sarsam0Hosam Al-Samarraie1Sunway University Business School, Sunway UniversityUniversity of LeedsThis 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.http://dx.doi.org/10.1080/08839514.2021.1993003 |
| spellingShingle | Samer Muthana Sarsam Hosam Al-Samarraie A lexicon-based method for detecting eye diseases on microblogs Applied Artificial Intelligence |
| title | A lexicon-based method for detecting eye diseases on microblogs |
| title_full | A lexicon-based method for detecting eye diseases on microblogs |
| title_fullStr | A lexicon-based method for detecting eye diseases on microblogs |
| title_full_unstemmed | A lexicon-based method for detecting eye diseases on microblogs |
| title_short | A lexicon-based method for detecting eye diseases on microblogs |
| title_sort | lexicon based method for detecting eye diseases on microblogs |
| url | http://dx.doi.org/10.1080/08839514.2021.1993003 |
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