What topics and emotions expressed by glaucoma patients? A sentiment analysis perspective

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.