An LSTM Model for Emotional Analyzing along with PHQ-9 for Identification of Depression

Abstract

The hypothesis of the present study is that detection of depression using sentiment analysis is possible and effective in text messaging applications. Innovations in technology facilitate health care management leading to huge saving of national wealth and human resources. The number of suicide cases annually in any given country is formidable and a cause of concern. The quality of education is impacted and the teaching-learning process is hindered when students feel depressed. If left untreated, depression can cause various health hazards and in the worst cases may lead to suicide. Hence, we worked on detecting depression through chat messages which is the most common and popular means of communication.