@Article{info:doi/10.2196/36118，作者=“Kabir, Muhammad Khubayeeb和Islam, Maisha和Kabir, Anika Nahian Binte和Haque, Adiba和Rhaman, Md Khalilur”，标题=“使用孟加拉语社交媒体帖子检测抑郁症严重程度:使用自然语言处理技术的研究”，期刊=“JMIR Form Res”，年=“2022”，月=“Sep”，日=“28”，量=“6”，数=“9”，页=“e36118”，关键词=“心理健康论坛”;自然语言处理;严重程度;重度抑郁症;深度学习;机器学习;背景:书面文本中有无数表明抑郁的语言线索，自然语言处理(NLP)研究人员已经证明了机器学习和深度学习方法检测这些线索的能力。然而，迄今为止，这些方法桥梁NLP和孟加拉文学的心理健康领域是不全面的。说孟加拉语的人可以用他们的母语更详细地表达情感。目的:我们的目标是通过生成一个新的孟加拉语抑郁帖子语料库来检测使用孟加拉语文本的抑郁症的严重程度。 We collaborated with mental health experts to generate a clinically sound labeling scheme and an annotated corpus to train machine learning and deep learning models. Methods: We conducted a study using Bengali text-based data from blogs and open source platforms. We constructed a procedure for annotated corpus generation and extraction of textual information from Bengali literature for predictive analysis. We developed our own structured data set and designed a clinically sound labeling scheme with the help of mental health professionals, adhering to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) during the process. We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). For the deep learning approach, we used long short-term memory (LSTM) units and gated recurrent units (GRUs) coupled with convolutional blocks or self-attention layers. Finally, we aimed for enhanced outcomes by using state-of-the-art pretrained language models. Results: The independent recurrent neural network (RNN) models yielded the highest accuracies and weighted F1 scores. GRUs, in particular, produced 81{\%} accuracy. The hybrid architectures could not surpass the RNNs in terms of performance. Kernel SVM with term frequency--inverse document frequency (TF-IDF) embeddings generated 78{\%} accuracy on test data. We used validation and training loss curves to observe and report the performance of our architectures. Overall, the number of available data remained the limitation of our experiment. Conclusions: The findings from our experimental setup indicate that machine learning and deep learning models are fairly capable of assessing the severity of mental health issues from texts. For the future, we suggest more research endeavors to increase the volume of Bengali text data, in particular, so that modern architectures reach improved generalization capability. ", issn="2561-326X", doi="10.2196/36118", url="https://formative.www.mybigtv.com/2022/9/e36118", url="https://doi.org/10.2196/36118", url="http://www.ncbi.nlm.nih.gov/pubmed/36169989" }