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เท 2018, The World Health Organization (WHO) and Department of Mental Health (DMH), specified that major depressive disorder (MDD) was the second most important disease that it is probably caused by social media usage affecting stress and leading to violence, and depression . This research proposes the depressive classification from posts on twitter of user behaviors and compared the accuracy of two classifiers between one level and two levels:- (1) one level: using the Bayes algorithm created a model for classification between general and symptoms based on a symptoms detailed in a questionnaire (DSM-5) including as follows: depression, loss of interest, loss of appetite, abnormal sleep, slowed thinking, guilt, tiredness, unexplained and suicidal ideation. (2) Two levels: Using the SVM algorithm created a model for classification between general and depression. Using the Bayes algorithm compared with the Random Forest algorithm for classification of symptoms เท a questionnaire (DSM-5). The data came from real postings of international celebrities. The dataset is divided into 2 sets: a training set and a test set. Finally, the results are demonstrated in a training set prediction between one level and two levels: One level: the Bayes algorithm showed that the accuracy=82.55%, and the SVM algorithm showed that the accuracy=96.18%. Two level: the SVM algorithm showed that the accuracy=98.20%. SVM algorithm pair with Bayes algorithm showed that the accuracy=82.23%, and SVM algorithm pair with the Random Forest algorithm showed that the accuracy=91.45%. The results of test set, by the boundary of probability are variously set 0.1 to 0.9 that prediction between one level and two levels : One level: the Bayes algorithm showed that the accuracy=76.67%, and the SVM algorithm showed that the accuracy=70.00%. Two level: SVM algorithm pair with Bayes algorithm showed that the accuracy=73.33%. SVM algorithm pair with Random Forest algorithm showed that the accuracy=70.00%. [ABSTRACT FROM AUTHOR]