Lexicon-based sentiment analysis of medical data
The article explores the possibilities of applying sentiment analysis for the use of information collected in the medical social media environment in medical decision-making. Opinions and feedbacks of medical social media subjects (physician, patient, health institution, etc.) make media resources an important source of information. The information collected in these sources can be used to improve the quality of health care and make decisions, taking into account the public opinion. Researches in this field have actualized the application of artificial intelligence methods, i.e., sentiment analysis methods. In this regard, it segments the medical social media environment in accordance with user relationships, and shows the nature of the information collected on each segment and its importance in decision-making to improve the quality of medical services. The possibilities of applying the lexicon-based sentiment analysis method for studying and classifying the collected data are explained in detail. The open database cms_hospital_satisfaction_2019 by the Kaggle company is used, and the opinions collected from patients about the services provided by a specific medical center are analyzed. This study analyzes opinions using the Valence Aware Dictionary and Sentiment Reasoner lexicon and classifies them as neutral, positive and negative and the implementation of this process is described in stages. The importance of the obtained results in decision-making regarding the better organization, evaluation and improvement of the activity of the medical institution is shown
Mammadova, M., Jabrayilova, Z. (2019). Electronic medicine: formation and scientific-theoretical problems. Baku: "Information Technologies" publishing house, 319. Available at: https://ict.az/uploads/files/E-medicine-monograph-IIT-ANAS.pdf
Mammadova, M., Isayeva, A. (2018). E-health activity in social media environment. Problems of Information Society, 09 (1), 52–62. doi: https://doi.org/10.25045/jpis.v09.i1.05
Mammadovа, M., Jabrayilova, Z., Isayeva, A. (2020). Conceptual Approach to the Use of Information Acquired in Social Media for Medial Decisions. Online Journal of Communication and Media Technologies, 10 (2). doi: https://doi.org/10.29333/ojcmt/7877
Issue Brief: Social Networks in Health Care: Communication, collaboration and insights. Produced by the Deloitte Center for Health Solutions. Available at: http://healthinformationandcommunicationsystems.pbworks.com/w/file/fetch/93972338/SM%204b%20Full.pdf
Fogelson, N. S., Rubin, Z. A., Ault, K. A. (2013). Beyond likes and tweets: an in-depth look at the physician social media landscape. Clinical Obstet Gynecol, 2013, 56 (3), 495–508.
Tibb.az. Your virtual doctor. Available at: https://tibb.az/home
Aattouchi, I., Elmendili, S., Elmendili, F. (2021). Sentiment Analysis of Health Care: Review. E3S Web of Conferences, 319, 01064. doi: https://doi.org/10.1051/e3sconf/202131901064
Khan, M. T., Khalid, S. (2015). Sentiment Analysis for Health Care. International Journal of Privacy and Health Information Management, 3 (2), 78–91. doi: https://doi.org/10.4018/ijphim.2015070105
Kausar, S., Huahu, X., Ahmad, W., Shabir, M. Y., Ahmad, W. (2020). A Sentiment Polarity Categorization Technique for Online Product Reviews. IEEE Access, 8, 3594–3605. doi: https://doi.org/10.1109/access.2019.2963020
Yevseiev, S., Goloskokova, A., Shmatko, O. (2021). Researching a machine learning algorithm for a face recognition system. Technology Transfer: Fundamental Principles and Innovative Technical Solutions, 10–12. doi: https://doi.org/10.21303/2585-6847.2021.002222
Hamdan, H., Bellot, P., Bechet, F. (2015). Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text. Research in Computing Science, 90 (1), 217–226. doi: https://doi.org/10.13053/rcs-90-1-17
Colab Research Google. Available at: https://colab.research.google.com/drive/17KLlgzCipalUtlID_ToGdoalKd-9A9OB#scrollTo=5N3Vro5vYyap
Ramya Sri, V. I. S., Niharika, Ch., Maneesh, K., Ismail, M. (2019). Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method. International Journal of Engineering and Advanced Technology, 9 (1), 6977–6981. doi: https://doi.org/10.35940/ijeat.a2141.109119
Rokade, P. P., D, A. K. (2019). Business intelligence analytics using sentiment analysis-a survey. International Journal of Electrical and Computer Engineering (IJECE), 9 (1), 613. doi: https://doi.org/10.11591/ijece.v9i1.pp613-620
U.S. Hospital Customer Satisfaction 2016-2020. Available at: https://www.kaggle.com/datasets/abrambeyer/us-hospital-customer-satisfaction-20162020
👁 127 ⬇ 115
Copyright (c) 2022 Masuma Mammadova, Zarifa Jabrayilova, Nargiz Shikhaliyeva
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.