  While sadness is a human emotion that people experience at certain times
throughout their lives, inflicting them with emotional disappointment and pain,
depression is a longer term mental illness which impairs social, occupational,
and other vital regions of functioning making it a much more serious issue and
needs to be catered to at the earliest. NLP techniques can be utilized for the
detection and subsequent diagnosis of these emotions. Most of the open sourced
data on the web deal with sadness as a part of depression, as an emotion even
though the difference in severity of both is huge. Thus, we create our own
novel dataset illustrating the difference between the two. In this paper, we
aim to highlight the difference between the two and highlight how interpretable
our models are to distinctly label sadness and depression. Due to the sensitive
nature of such information, privacy measures need to be taken for handling and
training of such data. Hence, we also explore the effect of Federated Learning
(FL) on contextualised language models.
