  Amid lockdown period more people express their feelings over social media
platforms due to closed third-place and academic researchers have witnessed
strong associations between the mental healthcare and social media posts. The
stress for a brief period may lead to clinical depressions and the long-lasting
traits of prevailing depressions can be life threatening with suicidal ideation
as the possible outcome. The increasing concern towards the rise in number of
suicide cases is because it is one of the leading cause of premature but
preventable death. Recent studies have shown that mining social media data has
helped in quantifying the suicidal tendency of users at risk. This potential
manuscript elucidates the taxonomy of mental healthcare and highlights some
recent attempts in examining the potential of quantifying suicidal tendency on
social media data. This manuscript presents the classification of heterogeneous
features from social media data and handling feature vector representation.
Aiming to identify the new research directions and advances in the development
of Machine Learning (ML) and Deep Learning (DL) based models, a quantitative
synthesis and a qualitative review was carried out with corpus of over 77
potential research articles related to stress, depression and suicide risk from
2013 to 2021.
