Towards this, we examine users on popular medical forums (,), where they post on important topics such as asthma, allergy, depression, and anxiety. In this paper, we address an important and timely topic by analyzing the users' sentiments and emotions w.r.t their medical conditions. A large percentage of this population is actively engaged in health social networks to share health-related information. The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text. Further research needs to be done in order to translate our quantitative and qualitative findings into features, as it may improve overall performance. It is possible to build a competitive triage classifier using features derived only from the textual content of the post. Six salient linguistic characteristics were found when analysing the crisis post (1) posts expressing hopelessness, (2) short posts expressing concise negative emotional responses, (3) long posts expressing variations of emotions, (4) posts expressing dissatisfaction with available health services, (5) posts utilising storytelling, and (6) posts expressing users seeking advice from peers during a crisis. When comparing with the state-of-the-art, a solution mainly based on features from lexical resources, received the best classification performance for the crisis posts (52%), which is the most severe class.

Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from mental health forum for young people. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use.

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