Lexicon-based sentiment analysis of medical data
Abstract
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
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References
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