Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.
People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed.Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.
Idiomatic expressions are part of everyday speech in many languages and text genres. However, identifying idiomatic expressions can be problematic for different NLP tasks as their meaning cannot be easily inferred from their constituting words. Although the automatic identification and understanding of idiomatic expressions are essential for Natural Language Understanding tasks, they are still largely underinvestigated. Until recently, the complex nature and lack of large datasets have prevented the development of machine learning approaches for identifying the idiomatic expressions. With the advancement of machine learning techniques, transformer-based language models such as DistilBERT, RoBERTa, and their variants have shown state of the art performance by capturing the compositionality of the textual representations many NLP tasks. In spite of the progress, these vector representations fail to identify the multiword expressions (MWEs) such as idioms. This study demonstrate that transformer-based language models using contextual embeddings perform much better than existing approaches when applied to texts associated with depression. The core of this article presents two subtasks: (a) binary classification or idiomatic expression identification using BERT sentence embeddings and (b) task based on the analysis of the idiomatic expression usage in depressive textual contents.
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