A Novel Ontology-Based Approach for Analyzing Patient Sentiment Regarding Chronic Diseases

Abstract

Sentiment analysis (SA) plays a central role in understanding the multidimensional nature of comments on social media platforms like YouTube, Twitter, Facebook, and health forums. This article presents a multi-aspect, multi-sentiment annotation of chronic disease-related comments, highlighting the importance of connecting aspects, such as disease category and treatment, with corresponding sentiments (positive, negative, and neutral). The integration of ontologies enhances semantic consistency, enabling a comprehensive understanding of user experiences related to chronic diseases. The purpose of this paper is to bridge the gap between unstructured patient-generated content and actionable insights for healthcare professionals. A structured approach employing two ontologies—one for chronic disease aspects and another for sentiment classification—enables the linking of disease-related comments with associated emotions, supporting comprehensive SA. A multi-label classification model is trained to simultaneously predict multiple aspects and sentiments within a single comment, addressing the native complexities of sentiment expression. The article concludes with an evaluation of the model’s predictions against real-world annotated data to assess its effectiveness. Our approach achieves promising results, demonstrating its ability to accurately link comments to their corresponding aspects and sentiments.