  Text sentiment analysis for preliminary depression status estimation of users
on social media is a widely exercised and feasible method, However, the immense
variety of users accessing the social media websites and their ample mix of
vocabularies makes it difficult for commonly applied deep learning-based
classifiers to perform. To add to the situation, the lack of adaptability of
traditional supervised machine learning could hurt at many levels. We propose a
cloud-based smartphone application, with a deep learning-based backend to
primarily perform depression detection on Twitter social media. The backend
model consists of a RoBERTa based siamese sentence classifier that compares a
given tweet (Query) with a labeled set of tweets with known sentiment (
Standard Corpus ). The standard corpus is varied over time with expert opinion
so as to improve the model's reliability. A psychologist ( with the patient's
permission ) could leverage the application to assess the patient's depression
status prior to counseling, which provides better insight into the mental
health status of a patient. In addition, to the same, the psychologist could be
referred to cases of similar characteristics, which could in turn help in more
effective treatment. We evaluate our backend model after fine-tuning it on a
publicly available dataset. The find tuned model is made to predict depression
on a large set of tweet samples with random noise factors. The model achieved
pinnacle results, with a testing accuracy of 87.23% and an AUC of 0.8621.
