An Analysis of Depression Detection Model Applying Data Mining Approaches Using Social Network Data

Abstract

Depression, also defined as major depressive disorder, is a broad and straightforward psychiatric disorder that affects how we feel, experience, and respond. Fortunately, it is curable. Depression causes symptoms of depression and/or a loss of confidence in previously enjoyed interests. Any year, one out of every 15 people (6.7 percent) suffers from depression. Even though one out of every six people (16.6 %) will experience depression at any stage in their life. Depression can strike at any age, although it is most frequent between late adolescence and the mid-twenties. It is very difficult to locate individuals who suffer from depression. We revealed that social media delivers valuable signs for characterizing the appearance of depression in persons, as determined by a decline in social interaction, improved depressive effect, heavily clustered ego N/w, heightened relational and medicinal issues, and greater expression of religious participation. In this paper we analyze the depressing text; Manipulate data: Extract their features and categorize them using of principal component analysis, sentiment analysis approach, and build a predictor using cross-validate with Machine Learning models (Like Multinomial naïve Bayes, K nearest neighbors, and SVM).In which we have found a 99.7% Success rate with the use of a Multinomial naïve Bayes classifier. We suggest that our experiments and interventions can be useful in developing approaches for predicting the beginning of serious depression, either for healthcare agencies or on behalf of individuals, helping depressed people to be more diligent about their mental health.