  Depression is a significant issue nowadays. As per the World Health
Organization (WHO), in 2023, over 280 million individuals are grappling with
depression. This is a huge number; if not taken seriously, these numbers will
increase rapidly. About 4.89 billion individuals are social media users. People
express their feelings and emotions on platforms like Twitter, Facebook,
Reddit, Instagram, etc. These platforms contain valuable information which can
be used for research purposes. Considerable research has been conducted across
various social media platforms. However, certain limitations persist in these
endeavors. Particularly, previous studies were only focused on detecting
depression and the intensity of depression in tweets. Also, there existed
inaccuracies in dataset labeling. In this research work, five types of
depression (Bipolar, major, psychotic, atypical, and postpartum) were predicted
using tweets from the Twitter database based on lexicon labeling. Explainable
AI was used to provide reasoning by highlighting the parts of tweets that
represent type of depression. Bidirectional Encoder Representations from
Transformers (BERT) was used for feature extraction and training. Machine
learning and deep learning methodologies were used to train the model. The BERT
model presented the most promising results, achieving an overall accuracy of
0.96.
