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
| Original language | English |
|---|---|
| Article number | 1993003 |
| Number of pages | 12 |
| Journal | Applied Artificial Intelligence |
| Volume | 36 |
| Issue number | 1 |
| Early online date | 21 Oct 2021 |
| DOIs | |
| Publication status | Published - Jan 2022 |
| Externally published | Yes |
Bibliographical note
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Keywords
- eye diseases
- microblogs
- lexicon-based approach
- time-series
- Apriori algorithm
- Deep learning