Discovering disease risk trends by applying Latent Dirichlet Allocation sentiment analysis to social media health discussions

Social media platforms generate large volumes of health-related content that can provide valuable insights into emerging disease concerns and public perceptions. This study explores disease-risk trends through topic modeling and sentiment analysis of social media discussions. We used Latent Dirichlet Allocation (LDA) to extract topics and VADER sentiments from 50,000 statements related to diseases. The model performance was evaluated in terms of coherence, topic diversity, and stability metrics. The comparison was made with NMF and BERTopic. Three main topics were identified: COVID-19 symptoms and diagnosis; chronic disease management; and disease risk factors. The LDA model obtained coherence and topic diversity scores of 0.402 and 0.77, respectively. The sentiment analysis shows that the discussions are overall neutral, with some sentiments related to negative concerns. The results indicate that the use of topic modeling along with sentiment analysis is a useful approach to extract major disease-related topics from social media data. Future studies using transformer-based models, multilingual data, and longitudinal studies may improve digital disease surveillance and enable proactive public health monitoring.