Diagnosis of Depressive Disorder Based on Facial and Text-Based Features Using Effective Techniques

Depression, which is one of the most common mental diseases in the world, has a significant and detrimental impact on society as a whole. Early detection of depression via the use of automated methods and assessments is very necessary in order to improve one's own physical state. Analysis of people's feelings is quickly becoming one of the most useful techniques we have for spotting signs of depression. Textual and natural language processing methods are used in sentiment analysis, with the end goal of extracting views and feelings hidden within the data. In this research, we examine the use of computers and methodologies for sentiment analysis, which will provide an effective way for diagnosing and monitoring mental illnesses like depression. Specifically, we focus on how these two areas may work together. This project shows and explores prospective techniques for emotional technologies that combine sentiment analysis with the capability of detecting and measuring depression. Additionally, a concept design for an integrated multimodal system for the diagnosis of sadness is given. This system makes use of sentiment analysis and affective computing approaches. This project had the goal of developing an application that could run on several platforms, be hosted in the cloud, and maintain high confidentiality while being independent of its operator. As our training data, we made use of word classifications in conjunction with specified characteristics of human facial motions. The combination of these two strategies results in a performance that is more effective overall. The correctness of the models that were detailed in the step before this one is tested using a variety of different algorithms, and the results of these tests will give us with a rough sketch of the right technique. Additional optimization may bring to a significant increase in accuracy.