Detection of suicidal intent in Spanish language social networks using machine learning

Suicide is a considerable problem in our population, early intervention for its prevention has a very important role, in order to counteract the number of deaths from suicide. Today, just over half of the world's population uses social networks, where they express ideas, feelings, desires, including suicide intentions. Motivated by these factors, the main objective is the automatic detection of suicidal ideations in social networks in the Spanish language, in order to serve as a base component to alert and achieve early and specialized interventions. For this, a Spanish suicide phrase classification model has been implemented, since currently no related works in this language with a machine learning approach were found. However, there were some challenges in performing this task, such as understanding natural language, generating training data, and obtaining reliable accuracy in classifying these phrases. To construct our classification model, two opposite and popular types of phrase embeddings were chosen, and the most widely used classification algorithms in the literature were compared. Obtaining, as a result, the confirmation that it is possible to classify phrases with suicidal ideation in the Spanish language with good accuracy using semantic representations.