An illustration of attention mechanism in LSTM algorithm for analyzing its performance

The advent of technologies and equipments in Information Technology and Communication has increased the use of smart phones and social media by all people irrespective of age and gender. Many times people find social media posts as the outlet for draining all their emotions. Depression is a common mental health problem that ranges from low mood and loss of interest to persistent feeling of sadness and self-destruction. Mental Health Experts are interested in analyzing the physical and social media behavior of patients to diagnose depression. Attention Mechanism imitates the human cognitive of considering only the most relevant data needed for a particular analysis. LSTM is a state-of-the-art Recurrent Neural Network architecture used for depression analysis. In this paper we are further improving the LSTM Algorithm by proposing an Attention Mechanism that makes the model robust and improved. This major role of our Attention Mechanism is identifying the Prime Words and Supporting words that are relevant for the analysis. Distance is calculated between non-prime words and prime words, weights are assigned based on this calculation, higher weight being given to most relevant words. Making use of Attention helps us to focus only on the relevant data and neglect the irrelevant data.&nbsp