A Python implementation of Twitter posts classifiers based on n-grams features evaluation. It is part of the University of Messina scientific literature about NLP and published as: Investigating Classification Supervised Learning Approaches for the Identification of Critical Patients' Posts in a Healthcare Social Network
Abstract: Nowadays, Healthcare Social Networks (HSNs) offer the possibility to enhance patient care and education. However, they also present potential risks for patients due to the possible distribution of poor-quality or wrong information along with their bad interpretation. On one hand doctors and practitioners want to promote the exchange of information among patients about a specific disease, but on the other hand they do not have enough time to read patients' posts and moderate them when required. In this paper, we investigate and compare different supervised learning classifiers that we adopted for the classification of critical patients' posts who can trigger the intervention of the medical personnel. In particular, by considering different Bayesian, Linear and Support Vector Machine (SVM) classifiers we analyze their accuracy considering different n-grams datasets preparation approaches in order to identify the best approach for the identification of critical patients' posts in a Healthcare Social Network.
The work is an extended, improved version of the paper Applying Artificial Intelligence in Healthcare Social Networks to Identity Critical Issues in Patients' Posts presented at AI4Health 2018 workshop and published in: BIOSTEC 2018, Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF, Funchal, Madeira, Portugal, 19-21 January, 2018, pp. 680-687, ISBN: 978-989-758-281-3, INSTICC, 2018.
By University of Messina, 2019