Mental Health Diagnosis in the Digital Age: Harnessing Sentiment
  Analysis on Social Media Platforms upon Ultra-Sparse Feature Content
Haijian Shao Ming Zhu Shengjie Zhai
Amid growing global mental health concerns, particularly among vulnerable
groups, natural language processing offers a tremendous potential for early
detection and intervention of people's mental disorders via analyzing their
postings and discussions on social media platforms. However, ultra-sparse
training data, often due to vast vocabularies and low-frequency words, hinders
the analysis accuracy. Multi-labeling and Co-occurrences of symptoms may also
blur the boundaries in distinguishing similar/co-related disorders. To address
these issues, we propose a novel semantic feature preprocessing technique with
a three-folded structure: 1) mitigating the feature sparsity with a weak
classifier, 2) adaptive feature dimension with modulus loops, and 3)
deep-mining and extending features among the contexts. With enhanced semantic
features, we train a machine learning model to predict and classify mental
disorders. We utilize the Reddit Mental Health Dataset 2022 to examine
conditions such as Anxiety, Borderline Personality Disorder (BPD), and
Bipolar-Disorder (BD) and present solutions to the data sparsity challenge,
highlighted by 99.81% non-zero elements. After applying our preprocessing
technique, the feature sparsity decreases to 85.4%. Overall, our methods, when
compared to seven benchmark models, demonstrate significant performance
improvements: 8.0% in accuracy, 0.069 in precision, 0.093 in recall, 0.102 in
F1 score, and 0.059 in AUC. This research provides foundational insights for
mental health prediction and monitoring, providing innovative solutions to
navigate challenges associated with ultra-sparse data feature and intricate
multi-label classification in the domain of mental health analysis.