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 common disease from social media platforms. The data were obtained using Twitter free streaming Application Programming Interface (API). cluster analysis applied extract instances that share similar characteristics. We extracted three types emotions (positive, negative, and neutral) users’ messages (tweets) SentiStrength. time-series method used determine applicability predicting emotional changes over a period seven months. relevant symptoms Apriori algorithm with prediction accuracy 98.89%. study offers timely effective can be implemented help healthcare decision makers researchers reduce spread in population specific manner.