A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media

• URL: http://arxiv.org/abs/2011.14280v1
• Date: Sun, 29 Nov 2020 04:42:53 GMT
• Title: A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
• Authors: Sudhir Kumar Suman, Hrithwik Shalu, Lakshya A Agrawal, Archit Agrawal, Juned Kadiwala
• Abstract summary: We propose a cloud-based smartphone application, with a deep learning-based backend to primarily perform depression detection on Twitter social media. A psychologist 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. The model achieved pinnacle results, with a testing accuracy of 87.23% and an AUC of 0.8621.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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