Analysis of Deep Learning Techniques for Early Detection of Depression on Social Media Network - A Comparative Study

Abstract

The early detection or identification of emotional states plays a vital role in today’s world, where the number of internet and social media users are increasing at an unprecedented rate. The psychiatric disorders are very dangerous and it is affecting 300 million people. This is the motivation behind addressing the research problem with novel research articles. Early detection is the key to reduce the number affected individuals due to this disorder potentially. This research study performs an analysis of a standard dataset obtained from online social media, where detection can be based on a machine learning algorithm. This research article proposes a machine-learning algorithm to develop an early prediction from their depression mode, which can be protected from mental illness and suicide state of affairs. The combination of support vector machine and Naïve Bayes algorithm will be used to provide a good accuracy level. The classification model contains many cumulative distribution parameters, which should be classified and identified dynamically. This identification or detection is the features obtained from textual, semantic, and writing content. The evaluation of various Deep Learning (DL) approaches is identifying the early prediction. The sensitivity and accuracy of the method are providing the significant conditions for early detection and late detection. The proposed hybrid method provides better results for early detection and retained good sensitivity and better accuracy of existing methods. The study from results can help to develop a new idea to develop a early prediction of various emotions of people present in social media.