Identification of depression signs in social media through natural language processing techniques

According to INCyTU [1], 450 million people in the world suffer from a mental disorder. Also, according to the WHO [2] depression is the most common mental disorder in people, it is also the leading global cause of disability and contributes significantly to the overall global burden of disease. Depression in critical cases can lead to suicide [3]. For this reason, it is important to develop mechanisms for the identification of depression signs in social media using Natural Language Processing techniques through analysis of emotions, feelings and polarity of the texts that users write. This will allow early detection of this mental disorder and timely treatment. Points to discuss: Background. Problem to solve. Proposed solution. Module for the identification of depression signs in social media. Model for depression detection. Case study. Conclusions. Future work. This work is related to the importance of detecting and treating mental health conditions and using the natural language processing of users' texts to recognize emotions or mental health conditions. The present work aims to support medical services for the detection of mental disorders that will improve the quality of life of people. Additionally, a reduction in the incidence of mental disorders is sought through the early identification of depression signs