Deep Learning Suicide Ideation Detection Model

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@article{1705022,
      author = {Kipkebut Andrew, Emmanuel Chesire},
      title = {Deep Learning Suicide Ideation Detection Model},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {3},
      pages = {44-51},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705022.pdf},
      abstract = {Suicidal Ideation in society today has become very common, this is due to stressful societal issues. Recently social platforms have gained special attention regarding this phenomenon. Mental health issues like depression, frustration, hopelessness, and bullying among others directly or indirectly influence suicidal thoughts. Early detection of suicidal intent can help people to diagnose and get proper treatment before it is too late. Deep Learning has played an important role in NLP-related predictions and detection. In this study, a novel detection approach that uses a deep learning approach is proposed. Essentially the study analyses raw natural language data from different sources such as social networks among others, and classifying the indication of suicidal ideation. This study focuses on Deep learning techniques as a base for suicidal ideation. The experiment shows that the BERT model can achieve an optimal classification result.},
      keywords = {Deep Learning, Suicide, Ideation, Natural Language Processing.},
      month = {September},
  }