Detecting and explaining suicidal ideation is a challenging task due to various factors, such as distinguishing between non-suicidal and suicidal language and interpreting ambiguous language related to suicide. Deep learning models, such as language transformers, have shown great promise in identifying suicidal ideation, but their complex structure limits their explainability.

To address this issue, this research paper presents an explainable deep learning approach to analyzing social media texts for detecting suicide ideation. The proposed approach utilizes deep learning using the BERT model to detect suicidal ideation, with a particular emphasis on explainability. The performance of these models is compared against traditional machine learning models like RF, SVM, Gaussian NB, LR, and k-nearest neighbors..

The research employs a dataset of social media posts containing suicide ideation and non-suicidal language to evaluate the models. The experimental results demonstrate that the proposed deep learning models outperform traditional machine learning models in terms of detection accuracy while also providing enhanced explainability.

In conclusion, the proposed approach provides an effective and explainable solution for detecting suicidal ideation in social media texts. The results highlight the potential of deep learning models, especially language transformers, in suicide prevention and mental health support. The approach can be extended to other forms of text analysis, such as identifying hate speech or cyberbullying, and can assist in the development of responsible and ethical AI systems.