Accurate classification of socially generated medical discourse

The growth of online health communities particularly those involving socially generated content can provide considerable value for society. Participants can gain knowledge of medical information or interact with peers on medical forum platforms. Analysing sentiment expressed by members of a health community in medical forum discourse can be of significant value, such as by identifying a particular aspect of an information space, determining themes that predominate among a large data set, and allowing people to summarize topics within a big data set. In this paper, we identify sentiments expressed in online medical forums that discuss Lyme disease. There are two goals in our research: first, to identify a complete and relevant set of categories that can characterize Lyme disease discourse; and second, to test and investigate strategies, both individually and collectively, for automating the classification of medical forum posts into those categories. We present a feature-based model that consists of three different feature sets: content-free, content-specific and meta-level features. Employing inductive learning algorithms to build a feature-based classification model, we assess the feasibility and accuracy of our automated classification. We further evaluate our model by assessing its ability to adapt to an online medical forum discussing Lupus disease. The experimental results demonstrate the effectiveness of our approach.