  The unprecedented growth of Internet users has resulted in an abundance of
unstructured information on social media including health forums, where
patients request health-related information or opinions from other users.
Previous studies have shown that online peer support has limited effectiveness
without expert intervention. Therefore, a system capable of assessing the
severity of health state from the patients' social media posts can help health
professionals (HP) in prioritizing the user's post. In this study, we inspect
the efficacy of different aspects of Natural Language Understanding (NLU) to
identify the severity of the user's health state in relation to two
perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate,
Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse
Effect, Other) in online health communities. We propose a multiview learning
framework that models both the textual content as well as
contextual-information to assess the severity of the user's health state.
Specifically, our model utilizes the NLU views such as sentiment, emotions,
personality, and use of figurative language to extract the contextual
information. The diverse NLU views demonstrate its effectiveness on both the
tasks and as well as on the individual disease to assess a user's health.
