Analyzing Emotional Stability on Social Media Post using Machine Learning Approach

Abstract

Depression or emotion is the most important concern of health organizations today. We consider the capability of influencing social media postings as a new type of mirror in understanding depression and also the type of personality in populations. Social network analysis is the study of a group of people and the relationships that exist between them. It has become so important in our lives that if I want to know anything about a stranger, I can find out with the help of social media websites. The arrival of various social media networking sites has helped everyone to easily express and share their opinions and feelings about anything with millions of people around the world. Social media is a valuable resource for identifying an individual's personality traits based on their posts, comments, or activities on social media. The proposed methodology, we have developed the application of a web extension to connect with social media networks to extract the post by the individual person. Extracted post has been used to identify the emotional stability of a person. NLP and Machine Learning algorithms are used to classify individual emotional stability as stable, depressed, or tending towards depression. According to our study, significant feature selections and their combinations were considered. Hence it improves the performance and accuracy of classification.