  Textual emotional intelligence is playing a ubiquitously important role in
leveraging human emotions on social media platforms. Social media platforms are
privileged with emotional content and are leveraged for various purposes like
opinion mining, emotion mining, and sentiment analysis. This data analysis is
also levered for the prevention of online bullying, suicide prevention, and
depression detection among social media users. In this article, we have
designed an automatic depression detection of online social media users by
analyzing their social media behavior. The designed depression detection
classification can be effectively used to mine user's social media interactions
and one can determine whether a social media user is suffering from depression
or not. The underlying classifier is made using state-of-art technology in
emotional artificial intelligence which includes LSTM (Long Short Term Memory)
and other machine learning classifiers. The highest accuracy of the classifier
is around 70% of LSTM and for SVM the highest accuracy is 81.79%. We trained
the classifier on the datasets that are widely used in literature for emotion
mining tasks. A confusion matrix of results is also given.
