Identifying Suicidal Risk: A Text Classification Study for Early Detection

Language usage is affected by suicidal intent that is conveyed on social media. Many at-risk users rely on online forum websites to discuss their issues or find out information about related duties. Our study’s main goal is to share ongoing research on automatically identifying suicidal postings. We developed a method in order to identify individuals who might be at suicide risk by analysing data from social networking sites like Reddit. To achieve this, we plan to apply a variety of classification techniques, including both deep learning and traditional machine learning methods. To this purpose, we compare our results to those of other classification methods using a combined LSTM-CNN model. Our experiment reveals that combining word embedding techniques with neural network architecture may produce the best relevance classification results. Furthermore, our results show how deep learning architectures may be used to build a viable model for a suicide risk assessment by excelling at a variety of text classification tasks.