  Depression is one of the most common and a major concern for society. Proper
monitoring using devices that can aid in its detection could be helpful to
prevent it all together. The Distress Analysis Interview Corpus (DAIC) is used
to build a metric-based depression detection. We have designed a metric to
describe the level of depression using negative sentences and classify the
participant accordingly. The score generated from the algorithm is then
levelled up to denote the intensity of depression. The results show that
measuring depression is very complex to using text alone as other factors are
not taken into consideration. Further, In the paper, the limitations of
measuring depression using text are described, and future suggestions are made.

Computation and Language