Depression detection using emotional artificial intelligence and machine learning: A closer review

Abstract

Depression is a prevalent mental disorder that can have a significant impact on people's mental health as well as their day-to-day lives. Depression and mental illness are a key problem in society nowadays. It can cause a loss of interest in general activities that can lead to suicidal thoughts. Hence, the need of an automated system that can help in detecting depression in people of various age groups is being realized. In order to detect depression,Researchers have been looking for approaches to effectively identify depression. A number of studies have been proposed in this regard. Here, In this study, we are analysing various existing studies based on Artificial Intelligence (AI) and diverse Machine Learning (ML) techniques being used to detect depression. Apart from it, different approaches used to detect emotion and mood in an individual are discussed. This study analyses how facial expressions, images, emotional chatbots and texts on social media platforms can be effectual in detecting one’s emotions and then depression. Naive-Bayes, Support Vector Machines (SVM), Long Term Short Memory (LSTM) – Radial Neural Networks (RNN), Logistic Regression, Linear Support Vector, etc. are the various ML techniques used to recognize emotions from text processing; Artificial Neural Network (ANN) is used for feature extraction and classifications of images to detect emotions through facial expressions. This paper aims to provide the survey of various AI and ML techniques which help in the detection and analysis of emotion and hence depression along with their related research issues.