Predicting post severity in mental health forums

We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. Our entry employs a meta-classifier which uses a set of of base classifiers constructed from lexical, syntactic and metadata features. These classifiers were generated for both the target posts as well as their contexts, which included both preceding and subsequent posts. The output from these classifiers was used to train a meta-classifier, which outperformed all individual classifiers as well as an ensemble classifier. This meta-classifier was then extended to a Random Forest of meta-classifiers, yielding further improvements in classification accuracy. We achieved competitive results, ranking first among a total of 60 submitted entries in the competition.