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

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...

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Bibliographic Details
Published in:arXiv.org 2020-11
Main Authors: Suman, Sudhir Kumar, Hrithwik Shalu, Agrawal, Lakshya A, Agrawal, Archit, Kadiwala, Juned
Format: Article
Language:English
Subjects:
Classifiers
Cloud computing
Data mining
Deep learning
Digital media
Machine learning
Mental depression
Mental health
Psychologists
Random noise
Sentiment analysis
Smartphones
Social networks
Websites
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Summary: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.
ISSN:2331-8422