Mental Health Monitoring using Sentiment Analysis

Authors

  • Rohan Dixit  Department of Information Technology, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India
  • Geetika Chawla  Department of Information Technology, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India
  • Ishaan Bajaj  Department of Information Technology, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India

DOI:

https://doi.org//10.32628/CSEIT228443

Keywords:

Mental Health, Natural Language Processing, Sentiment Analysis, Health Monitoring System, Mental Disorders, Smart Phones, social media, Lambda Architecture.

Abstract

After a lot of research, there is no doubt in saying that overall physical, psychological and communal welfare of a human being is predominantly dependent on their mental health. Thus, early recognition and mediation in addressing issues regarding it should be our topmost priority. Individualized and ubiquitous sensing technologies such as smartphones, smartwatches, activity trackers, etc. allow continuous trackingand gathering of data in an undisturbed and low-profilemanner. Sentimental Analysis using Natural Language Processing has been proposed to be applied to the collected data to predict user information such as mood, activity, mental status, depression, anxiety, stress. The intent of this survey is to analyze and propose a methodology for data extraction and a model using Lambda Architecture to study the social media presence and other available data to predict the mental state of the user along with taking additional measures to maintain the secrecy of the user along with dataprivacy.

References

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Published

2022-08-30

Issue

Section

Research Articles

How to Cite

[1]
Rohan Dixit, Geetika Chawla, Ishaan Bajaj, " Mental Health Monitoring using Sentiment Analysis, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.324-330, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT228443