• Happiness Index: a new method was developed using the tweets. Feel-Good-Factors computed that gave an insight into the mental status of the people. These will be useful to reinforce the mental health policies and Return On Investment (ROI) to scale-up the prioritized sectors with effective interventions.
• A new paradigm parameter “Depression Index” was developed using the depression tweets that monitors the mental health of twitter users. Using F1 and MCC, we developed the depression index, which is useful to find the relative depression rates. This is applied to different geographical areas and used in specific events.
• A method was developed to predict the mental health of twitter users during COVID-19 pandemic period. Classification of covid epidemic curve was observed. This showed a good similarity with WHO Reports.
• Developed a new method to cluster the keywords using the tweets during an event: A case study was done Srilanka Bomb blasts.
• Developed a method to find depressive Twitter User by Analyzing Time Series Tweets. Inter-tweet duration is considered, and depressive user is identified.
• Developed a method to find depressive twitter user by analyzing the depress and anti-depress tweets. Tweet text is analyzed to identify a user.
• Developed a new method and analyzed the tweets to Discover Twitter Users’ Mental Health Status by a Word-Frequency Method.
• An Online Mental Illness Detection method was developed with decision trees.

Reviewer for the International Conference on Recent Trends in Electrical Electronics communication and Instrumentation (ICRTEECI 2021), Hyderabad India