Machine Learning Approach Depression Intensit...

Machine Learning Approach Depression Intensity Estimation via Social Media: A Review

Author Name : Mrs. Asmita Thube, Prof. Anisara Nadaph, Prof. Sneha Tirth

ABSTRACT

Use social media to detect depression in real time is the aim of this research. During the ongoing COVID-19 pandemic and numerous lockdowns, mental health has been a big concern. We proposed a deep learning strategy for assessing the severity of depression based on social media data. The goal of this project is to use social media to determine the severity of depression in real time, so that the correct therapy may be prescribed based on the level of depression. Self-supervised, we developed a relabeling technique for a benchmark depression data set, established a rich collection of discriminative depression characteristics for Twitter's users, and suggested an LSTM network for detecting various levels of depressed Twitter users. Our solution outperformed the other methods for estimating intensity in extensive studies on a standard data set. There is more than a 2% difference between our methodology and the current binary classification method. The findings of this study indicate to a number of possible future directions. It would be fascinating to see how depression spreads among social groups.

Keywords— Social Media, Machine Learning, Depression detection, Medical.