BDI using NLP for Efficient Depression Identification
Keywords:
Beck Depression Inventory (BDI), Natural Language Processing(NLP), Depression, Machine LearningAbstract
This study aimed to assess the prevalence and nature of mental disorders by attending the physicians. Mental
disorder leads to difficulties in educational, social, occupational fileds. Failure to detect mental disorder denies patients
give proper treatment. So the main aim of our projects is to analyses the symptoms of individuals and applies each
permutation to the situation to detect the disordered person. In our project, the input will be given in the form of speech.
The speech will be converted to text using Google API. Then by applying NLP to text, sentiment analysis will do using
BDI questions from the person will be asked. The result generated will be stored. From that response find out that the
person is normal or in depressed state. If the result generated are negative that is the person is found in depresses state,
then we will suggest that person some measures to come out that state. The measure suggested can be like visiting a
physician, doing exercise or doing things of interest.
