مع الصحة العقلية كملم مشكلة في NLP، يدور الجزء الأكبر من الأدب المعاصر حول بناء نماذج تنبؤات أمرية أفضل. كان البحث التركيز على تحديد مجموعات المناقشة في مجتمعات الصحة العقلية عبر الإنترنت محدودا نسبيا. علاوة على ذلك، نظرا لأن المنهجيات الأساسية المستخدمة في هذه الدراسات تتفق بشكل أساسي مع نماذج تعليم الآلة التقليدية والأساليب الإحصائية، فإن نطاق إدخال تمثيلات الكلمات السياقية لموضوع استخراج الموضوع والشيء من المجتمعات الصحية العقلية عبر الإنترنت مفتوحة. وهكذا، في هذا البحث، نقترح تمثيل موضوعي عميق مدعوم، وهي تقنية تمثيل بيانات رواية تستخدم ABLENCODERS لجمع بين المدينات السياقية العميقة مع المعلومات الموضعية، وتوليد تمثيلات قوية للتجميع النصي. التحقيق في الخطاب Reddit على اضطراب ما بعد الصدمة الاضطرابات (PTSD) واضطراب الإجهاد بعد الصدمة المعقدة (C-PTSD)، ونحن نرفض المجموعات المواضيعية التي تمثل المواضيع والسمات الكامنة التي تمت مناقشتها في Subretits R / PTSD و R / CPTSD. علاوة على ذلك، نقدم أيضا تحليلا نوعيا وتوصيف كل كتلة، وكشف مواضيع الخطاب السائدة.
With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communities has been relatively limited. Moreover, as the underlying methodologies used in these studies mainly conform to the traditional machine learning models and statistical methods, the scope for introducing contextualized word representations for topic and theme extraction from online mental health communities remains open. Thus, in this research, we propose topic-infused deep contextualized representations, a novel data representation technique that uses autoencoders to combine deep contextual embeddings with topical information, generating robust representations for text clustering. Investigating the Reddit discourse on Post-Traumatic Stress Disorder (PTSD) and Complex Post-Traumatic Stress Disorder (C-PTSD), we elicit the thematic clusters representing the latent topics and themes discussed in the r/ptsd and r/CPTSD subreddits. Furthermore, we also present a qualitative analysis and characterization of each cluster, unraveling the prevalent discourse themes.
References used
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