  Researchers use Twitter and sentiment analysis to predict Cardiovascular
Disease (CVD) risk. We developed a new dictionary of CVD-related keywords by
analyzing emotions expressed in tweets. Tweets from eighteen US states,
including the Appalachian region, were collected. Using the VADER model for
sentiment analysis, users were classified as potentially at CVD risk. Machine
Learning (ML) models were employed to classify individuals' CVD risk and
applied to a CDC dataset with demographic information to make the comparison.
Performance evaluation metrics such as Test Accuracy, Precision, Recall, F1
score, Mathew's Correlation Coefficient (MCC), and Cohen's Kappa (CK) score
were considered. Results demonstrated that analyzing tweets' emotions surpassed
the predictive power of demographic data alone, enabling the identification of
individuals at potential risk of developing CVD. This research highlights the
potential of Natural Language Processing (NLP) and ML techniques in using
tweets to identify individuals with CVD risks, providing an alternative
approach to traditional demographic information for public health monitoring.
