A comparison of neural word embedding language models for classifying social media users in the healthcare context

n the era of generalist social media, finding users who share the same diseases and the same related experiences during their course is one of the main objectives of patients. In this reference framework, in applications related to recommender systems or infoveillance, just to name a few, it is useful to synthesize language models capable of capturing the semantic relationships in short texts written by patients in various posts, with the dual goal of training well-performing classification systems. In this work, a series of semantic text representation approaches - both traditional and advanced - are compared through NLP techniques, in order to classify Italian users belonging to discussion groups on medical topics. The classification and semantic evaluation experiments of the models are satisfactory above all, especially by considering that the collected dataset is unbalanced.