DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

¹Gayatri Shinde, ²Priyanka Date, ³Swati Hire, ⁴Punam Sanap,

^1,2,3,4Student, ⁵Professor, ^1,2,3,4,5Computer Engineering,

^1,2,3,4,5Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.

Abstract. Nowadays the problem of early depression detection is one of the most essential withinside the concern of psychology. Mental health issues are widely not unusual to place as one of the most prominent health stressful conditions withinside the world, with over 3 hundred million humans currently affected by depression alone. With huge volumes of man or woman-generated records on social networking platforms, researchers are growing variety the use of gadgets gaining know-how to determine whether or not or now no longer this content material cloth can be used to find out highbrow health problems in clients. Depression is a disorder that has been a superb concern in our society and has been continuously a heating concern relying on researchers withinside the world. Despite the huge quantity of assessments on know-how character moods together with depression, anxiety, and strain supported hobby logs collected thru pervasive computing devices like smartphones, foretelling depressed moods continues to be an open question. Social networks assessment is widely executed to address this problem. In this paper, we have got were given proposed a depression assessment and a suicidal ideation detection system, for predicting the suicidal acts that supported the extent of depression. The present examination aims to make the maximum tool for getting to know techniques for detecting a possible depressed Social Media man or woman in his/her Posts. For this purpose, we knowledgeable and tested classifiers to differentiate whether or not or now no longer someone is depressed or now not the use of competencies extracted from his/her sports activities withinside the posts. kind tool algorithms are used to train and classify it in Different tiers of depression on a scale of 0-100%. Also, records end up collected withinside the form of posts and have been categorized into whether or not or now no longer the most effective that tweeted is in depression or now not the use of kind algorithms of Machine Learning In this way Predictive method for early detection of depression or exceptional highbrow illnesses. This examination’s number one contribution is the exploration of a network of competencies and its impact on detecting the Depression degree. This examination aims to increase a deep getting to know the model to categorize clients with depression via a couple of instances getting to know, that would study from man or woman-degree labels to find out post-degree labels. By combining every possibility of posts label category, it can generate temporal posting profiles that would then be used to categorize clients with depression. This paper shows that there are smooth versions in posting patterns amongst clients with depression and non-depression, that's represented thru the combined opportunity of posts label category. In this study, the tool gaining know-how is used to system the scrapped records collected from social media clients' posts. Natural Language Processing (NL P), categorized the use of the BERT set of regulations to find out depression probably in a greater on hand and inexperienced way

Keywords: Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post.