Depression Disorder Detection Using Facial Expression and Text Mining on Recorded Social Media Videos

Abstract

Suicide is one of the most serious public health problems in modern culture. Thoughts of suicide, also known as suicidal thoughts, refer to people's suicidal plans. It can be used as a suicide risk measure. India is one of the top countries in the world with an annual suicide rate. Social networks have been developed as a first way to measure its users to connect with their interested friends and to rate their captions, photos, and videos that express their feelings, feelings and emotions. Enlarge and intensify the version that takes the form of facial images such as inserts and symbols. On the basis of what it predicts the patient's reputation whether or not he or she has not been diagnosed due to depression. We can train the translation using pictures and we will use it to predict. Image caption can be completed after predicting to get a higher visibility of the report. We will also use the text mining process (NLP) to predict the melancholy use of symbols provided with human assistance. Finally we are able to make the final choice primarily based on the two strategies above. Generate a detailed dashboard of user status and design webpage for an advanced program. We will use CNN's algorithm to accelerate the detection of depressed characters and how to know about high-quality responses to mental health problems. We propose a system learning approach as an effective and measurable method. We are writing the implementation of the proposed approach. We evaluated the effectiveness of our proposed approach to the use of a set of different aspects of mental language. We demonstrate that our proposed approach can significantly improve the accuracy and pricing of class errors.