Abstract: In today’s world social network play a vital role and provides relevant information on user opinion. This study presents emotional health monitoring system to detect stress and the user mood. While it has often been difficult for those outside a network of family and friends to identify persons who may be at risk of suicide, we can turn to Web2.0 and the blogs hosted on social networking sites to give a helping hand. Blogs such as those on my space have been the focus of many high-profile youth suicide cases in recent years, where suicidal youth have posted messages prior to taking their own lives. This problem is traditionally solved by using machine learning approaches. For instance, sentences can be classified according to their readability, using pre-built features and classification algorithms like SVM, Random Forest and others. Depending on results the system will send happy, calm, relaxing or motivational messages to users with psychological disturbance. It also sends warning messages to authorized persons incase a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorithm.