1,177
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A lexicon-based method for detecting eye diseases on microblogs

&
Article: 1993003 | Received 27 Apr 2021, Accepted 27 Sep 2021, Published online: 21 Oct 2021

References

  • Ahmet, A., and T. Abdullah. 2020. Recent trends and advances in deep learning-based sentiment analysis. In Deep learning-based approaches for sentiment analysis, 117–128. Singapore: Springer.
  • Ahn, J. M., S. Kim, K.-S. Ahn, S.-H. Cho, K. B. Lee, and U. S. Kim. 2018. A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PloS One 13 (11):e0207982. doi:10.1371/journal.pone.0207982.
  • Al-Ajeli, A., R. Alubady, and E. S. Al-Shamery. 2020. Improving spam email detection using hybrid feature selection and sequential minimal optimization. Indonesian Journal of Electrical Engineering and Computer Science 19 (1):535–42. doi:10.11591/ijeecs.v19.i1.pp535-542.
  • Al-Samarraie, H., S. M. Sarsam, A. I. Alzahrani, and N. Alalwan. 2018. Personality and individual differences: The potential of using preferences for visual stimuli to predict the Big Five traits. Cognition, Technology & Work 20 (3):337–49. doi:10.1007/s10111-018-0470-6.
  • Al-Samarraie, H., S. M. Sarsam, and H. Guesgen. 2016. Predicting user preferences of environment design: A perceptual mechanism of user interface customisation. Behaviour & Information Technology 35 (8):644–53. doi:10.1080/0144929X.2016.1186735.
  • Ban, N., C. J. Siegfried, and R. S. Apte. 2018. Monitoring neurodegeneration in glaucoma: Therapeutic implications. Trends in Molecular Medicine 24 (1):7–17. doi:10.1016/j.molmed.2017.11.004.
  • Bartlett, J. D., M. S. Keith, L. Sudharshan, and S. J. Snedecor. 2015. Associations between signs and symptoms of dry eye disease: A systematic review. Clinical Ophthalmology (Auckland, NZ) 9:1719. doi:10.2147/OPTH.S89700.
  • Chen, J., M. S. Hossain, and H. Zhang. 2020. Analyzing the sentiment correlation between regular tweets and retweets. Social Network Analysis and Mining 10 (1):1–9. doi:10.1007/s13278-020-0624-4.
  • Ciuraru. (2016). anxiety and defense mechanisms of people diagnosed with glaucoma.
  • Culpeper, J., A. Findlay, B. Cortese, and M. Thelwall. 2018. Measuring emotional temperatures in Shakespeare’s drama. English Text Construction 11 (1):10–37. doi:10.1075/etc.00002.cul.
  • Denecke, K., and Y. Deng. 2015. Sentiment analysis in medical settings: New opportunities and challenges. Artificial Intelligence in Medicine 64 (1):17–27. doi:10.1016/j.artmed.2015.03.006.
  • Dervisevic, E., S. Pavljasevic, A. Dervisevic, and S. S. Kasumovic. 2016. Challenges in early glaucoma detection. Medical Archives 70 (3):203. doi:10.5455/medarh.2016.70.203-207.
  • Dorison, C. A., K. Wang, V. W. Rees, I. Kawachi, K. M. Ericson, and J. S. Lerner. 2020. Sadness, but not all negative emotions, heightens addictive substance use. Proceedings of the National Academy of Sciences 117 (2):943–49. doi:10.1073/pnas.1909888116.
  • Jabbehdari, S., J. L. Chen, and T. S. Vajaranant. 2021. Effect of dietary modification and antioxidant supplementation on intraocular pressure and open-angle glaucoma. European Journal of Ophthalmology 31 (4):1588-1605.
  • Jampel, H. D., K. D. Frick, N. K. Janz, P. A. Wren, D. C. Musch, R. Rimal, P. R. Lichter, and C. S. Group. 2007. Depression and mood indicators in newly diagnosed glaucoma patients. American Journal of Ophthalmology 144 (2):238–44. e231. doi:10.1016/j.ajo.2007.04.048.
  • Kabiraj, S., L. Akter, M. Raihan, N. J. Diba, E. Podder, and M. M. Hassan (2020). Prediction of recurrence and non-recurrence events of breast cancer using bagging algorithm. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.
  • Kessler, S. H., and S. Schmidt-Weitmann. 2021. Diseases and emotions: An automated content analysis of health narratives in inquiries to an online health consultation service. Health Communication 36 (2):226–35. doi:10.1080/10410236.2019.1673950.
  • Khaira, U., R. Johanda, P. E. P. Utomo, and T. Suratno. 2020. Sentiment analysis of cyberbullying on twitter using sentistrength. Indonesian Journal of Artificial Intelligence and Data Mining 3 (1):21–27. doi:10.24014/ijaidm.v3i1.9145.
  • Kim, S. J., K. J. Cho, and S. Oh. 2017. Development of machine learning models for diagnosis of glaucoma. PloS One 12 (5):e0177726. doi:10.1371/journal.pone.0177726.
  • Maetschke, S., B. Antony, H. Ishikawa, G. Wollstein, J. Schuman, and R. Garnavi. 2019. A feature agnostic approach for glaucoma detection in OCT volumes. PloS One 14 (7):e0219126. doi:10.1371/journal.pone.0219126.
  • Mehta, P., C. A. Petersen, J. C. Wen, M. R. Banitt, P. P. Chen, K. D. Bojikian, C. Egan, S.-I. Lee, M. Balazinska, and A. Y. Lee. 2021. Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images. American Journal of Ophthalmology 231:154–69. doi:10.1016/j.ajo.2021.04.021.
  • Moorhead, S. A., D. E. Hazlett, L. Harrison, J. K. Carroll, A. Irwin, and C. Hoving. 2013. A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research 15 (4):e1933. doi:10.2196/jmir.1933.
  • Rasmussen, H. M., and E. J. Johnson. 2013. Nutrients for the aging eye. Clinical Interventions in Aging 8:741.
  • Ru, B., and L. Yao. 2019. A literature review of social media-based data mining for health outcomes research. Social Web and Health Research, 1:1–14.
  • Sarsam, S. M., H. Al-Samarraie, and A. Al-Sadi. 2020. Disease discovery-based emotion lexicon: A heuristic approach to characterise sicknesses in microblogs. Network Modeling Analysis in Health Informatics and Bioinformatics 9 (1):1–10. doi:10.1007/s13721-020-00271-6.
  • Sarsam, S. M., H. Al-Samarraie, and A. I. Alzahrani. 2021. Influence of personality traits on users’ viewing behaviour. Journal of Information Science 1:0165551521998051.
  • Sarsam, S. M., H. Al-Samarraie, A. I. Alzahrani, and B. Wright. 2020a. Sarcasm detection using machine learning algorithms in Twitter: A systematic review. International Journal of Market Research 62 (5):578–98. doi:10.1177/1470785320921779.
  • Sarsam, S. M., H. Al-Samarraie, A. I. Alzahrani, W. Alnumay, and A. P. Smith. 2021a. A lexicon-based approach to detecting suicide-related messages on Twitter. Biomedical Signal Processing and Control 65:102355. doi:10.1016/j.bspc.2020.102355.
  • Sarsam, S. M., H. Al-Samarraie, N. Bahar, A. S. Shibghatullah, A. Eldenfria, and A. Al-Sa’Di (2021b). Detecting real-time correlated simultaneous events in microblogs: The Case of Men’s Olympic Football. International Conference on Human-Computer Interaction, Washington DC, USA.
  • Sarsam, S. M., H. Al-Samarraie, N. Ismail, F. Zaqout, and B. Wright. 2020b. A real-time biosurveillance mechanism for early-stage disease detection from microblogs: A case study of interconnection between emotional and climatic factors related to migraine disease. Network Modeling Analysis in Health Informatics and Bioinformatics 9:1–14. doi:10.1007/s13721-020-00239-6.
  • Smailhodzic, E., W. Hooijsma, A. Boonstra, and D. J. Langley. 2016. Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Services Research 16 (1):1–14. doi:10.1186/s12913-016-1691-0.
  • Smeriglio, A., D. Barreca, E. Bellocco, and D. Trombetta. 2016. Chemistry, pharmacology and health benefits of anthocyanins. Phytotherapy Research 30 (8):1265–86. doi:10.1002/ptr.5642.
  • Stamatiou, M.-E., D. Kazantzis, P. Theodossiadis, and I. Chatziralli. 2021. Depression in glaucoma patients: A review of the literature. Seminars in Ophthalmology 1–7. doi:10.1080/08820538.2021.1903945.
  • Stefan, A.-M., E.-A. Paraschiv, S. Ovreiu, and E. Ovreiu. (2020, October). A Review of Glaucoma Detection from Digital Fundus Images using Machine Learning Techniques. In 2020 International Conference on e-Health and Bioengineering (EHB) (pp. 1-4). IEEE.
  • Su, L. Y.-F., M. A. Cacciatore, X. Liang, D. Brossard, D. A. Scheufele, and M. A. Xenos. 2017. Analyzing public sentiments online: Combining human-and computer-based content analysis. Information, Communication & Society 20 (3):406–27. doi:10.1080/1369118X.2016.1182197.
  • Thelwall, M. 2017. The heart and soul of the web? Sentiment strength detection in the social web with SentiStrength. In Cyberemotions, 119–34. Springer.
  • Witten, I. H., E. Frank, M. A. Hall, and C. J. Pal. 2016. Data mining: Practical machine learning tools and techniques. United States: Morgan Kaufmann.
  • Zhang, X., and S. Zhou. 2020. Sharing health risk messages on social media: Effects of fear appeal message and image promotion. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 14 (2):2. doi:10.5817/CP2020-2-4.