Depression Detection by Analyzing Social Media Post of User

Nowadays the problem of early depression detection is one of the most important in the field of psychology .Mental health issues are widely accepted as one of the most prominent health challenges in the world, with over 300 million people currently suffering from depression alone. With massive volumes of user-generated data on social networking platforms, researchers are increasingly using machine learning to determine whether this content can be used to detect mental health problems in users. Depression as a disorder has been an excellent concern in our society and has been continuously a hot topic for researchers in the world. Despite the massive quantity of analysis on understanding individual moods together with depression, anxiety, and stress supported activity logs collected by pervasive computing devices like smartphones, foretelling depressed moods continues to be an open question. Social networks analysis is widely applied to address this problem. In this paper, we have proposed a depression analysis and suicidal ideation detection system, for predicting the suicidal acts supported the extent of depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Social Media user his/her Posts. For this purpose, we trained and tested classifiers to differentiate whether a user is depressed or not using features extracted from his/her activities within the posts. classification machine algorithms are used to train and classify it in Different stages of depression on scale of 0-100%. Also, data was collected in the form of posts and were classified into whether the one that tweeted is in depression or not using classification algorithms of Machine Learning In this way Predictive approach for early detection of depression or other mental illnesses. This study’s main contribution is that the exploration a neighborhood of the features and its impact on detecting Depression level. This study aims to develop a deep learning model to classify users with depression via multiple instance learning, which can learn from user-level labels to identify post-level labels. By combining every possibility of posts label category, it can generate temporal posting profiles which can then be used to classify users with depression. This paper shows that there are clear differences in posting patterns between users with depression and non-depression, which is represented through the combined likelihood of posts label category. In this research, machine learning is used to process the scrapped data collected from social media users posts. Natural Language Processing (NLP), classified using BERT algorithm to detect depression potentially in amore convenient and efficient way. Keywords: Machine Learning, NLP, BERT Algorithm, Classification, Social Media Post