Depression is a common mental issue that is suffered by many whether they realised it or not. Early on set of depression could be easily ignored, however if it turns severe, it could result in more dangerous behaviors. Therefore, it is helpful if there could be some warning signs to indicate to a person whether they are depressed or not and enable them to seek help soon. In this work, a journaling system with embedded text depression detection is proposed. Although it cannot be used as a formal diagnosis, but it serves as a warning to the users. A personal journal or diary enables someone to release and express all their inner thoughts and feelings. It is where there are genuine expressions of feelings. By implementing text depression detection in the journal system, honest feelings are able to be captured and analysed to detect any early signs of onset depression. The system will embed a machine learning model which analyses and classifies texts as depressed or non-depressed. An experiment is performed to compare the performance between several machine learners and Logistic Regression showed better performance and was implemented in the system. Tests were performed on the system to evaluate the system functionality and it was seen that the system could perform well and classify texts accurately.