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.

通过分析社交媒体平台上人们的发帖和讨论，借助自然语言处理技术，本研究提出一种新颖的语义特征预处理技术，通过弱分类器减少特征稀疏性，采用模量循环实现自适应特征维度，深度挖掘和扩展上下文中的特征，训练一个机器学习模型来预测和分类精神障碍，通过 Reddit 精神健康数据集 2022 对焦虑、边缘型人格障碍和双相情感障碍等病症进行研究，解决了数据稀疏性挑战，显著提高了性能，为心理健康预测与监测提供创新解决方案。