Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique

Human emotions like depression are inner sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these type of emotions from people's social activities in virtual world can be very helpful to understand their behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative or neutral expressions. However, human emotions, such as depression, are very critical and sometimes almost impossible to analyze using these approaches. In this work, we deployed Long Short Term Memory (LSTM) Deep Recurrent Network for depression analysis on Bangla social media data. We created a small dataset of Bangla tweets and stratified it. In this paper, we have shown the effects of hyper-parameter tuning and how it can be helpful for depression analysis on a small Bangla social media dataset. The result shows that 5 layered LSTM of size 128 with batch size 25, learning rate 0.0001 over 20 epochs, the depression detection accuracy is high for stratified dataset with repeated sampling. This result will help psychologists and other researchers to detect depression of individuals from their social activities in virtual world and help them to take necessary measures to prevent undesirable doings resulted from depression.