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This paper introduces DeMHeM, a novel multitask framework designed for the descriptive classification of bipolar and related mental health topics on online platforms like Reddit. The model distinguishes between different mental health categories and also models the correlation among them by categorizing each post into potentially multiple mental health categories. DeMHeM leverages both the shared latent and task-specific semantic feature space by integrating sentence-level and topic-level embeddings. It further incorporates Focal Loss for joint learning, inter-task parameter sharing, and regularization decay to optimize the prediction for the naturally skewed imbalanced dataset. Through extensive experiments and a comprehensive ablation study, we demonstrate the effectiveness of our model, with results outperforming existing baselines. Furthermore, a case study is conducted to analyze the entirety of the "r/bipolar" subreddit to understand the specific nuances in the discussion of bipolar disorder by applying our model to data collected from January 2020 to December 2021 and extracting the top keywords of each predicted category. Our analysis shows that DeMHeM can be used to understand the multi-faceted discussion of mental health topics for a given community.