A lexicon-based method for detecting eye diseases on microblogs

Samer Sarsam, Hosam Al-Samarraie

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 languageEnglish
Article number1993003
Number of pages12
JournalApplied Artificial Intelligence
Volume36
Issue number1
Early online date21 Oct 2021
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

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

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