No abstract

No abstract

The analysis of microblogging data has been widely used to discover valuable resources for timely identification of critical illness-related incidents and serious epidemics. Despite the numerous efforts made in this field, making an accurate and timely prediction of incidents and outbreaks based on certain clinical symptoms remains a great challenge. Hence, providing an investigative method can be crucial in characterising a disease state. This study proposes a heuristic mechanism by using an unsupervised learning technique to efficiently detect disease incidents and outbreaks from the tweet content. We categorised the types of emotions that are highly linked to a specific disease and its related terminologies. Emotions (anger, fear, sadness, and joy) and diabetes-related terminologies were extracted using the NRC Affect Intensity Lexicon and a part-of-speech tagging tool. A two-cluster solution was established and validated.The classification result showed that K-means clustering with 2 centroids had the highest classification accuracy (96.53%). The relationship between diabetes-related terms (in the form of tweets) and emotions were established and assessed using the association rules mining technique.The results showed that diabetes-related terms were exclusively associated with fear emotions.This study offers a novel mechanism for disease recognition and outbreak detection in microblogs which is useful in making informed decisions about a disease state.

No abstract

No abstract