Analysing Mental Health Across Twitter Users by Sentiment Analysis
Ansh Agrawal
, Abhinav raj , Anuj Kumar rai , Milan Pandey
Semtimental Analysis, Twitter, Depression, Mental Health, Social Networking
Social networking services like Twitter provide an abstract representation of one's mental condition. The prevalence of mental health illnesses frequently goes undiagnosed, creating a severe problem that still affects all facets of society. Popular social networking websites can be used to spot recurring psychological trends. These patterns can represent one's daily thoughts and emotions. Our study uses Twitter data to identify people who may be experiencing mental problems and categorise them using sentiment analysis techniques based on the language used and certain behavioural characteristics. We present a novel approach for data extraction and concentrate on the study of depression, schizophrenia, anxiety disorders, substance misuse, and seasonal affective disorders in order to address the growing issue of mental disorders. By monitoring users on Twitter for a predetermined amount of time, our technology may be utilised to not only identify but also measure users' advancement. In the long run, this can assist medical practitioners and public health specialists in tracking the signs and patterns of development of mental problems in social media users. The use of social media exacerbates issues with mental health. The consequences of social network use on mental health are summarised in this comprehensive study. Google Scholar databases produced a selection of 50 papers; following the application of various inclusion and exclusion criteria, 16 papers were selected, and each paper was assessed for quality. The remaining eight papers were systematic reviews, three were longitudinal studies, two were qualitative studies, and eight were cross-sectional studies. Anxiety and depression were categorised as two mental health outcomes. Spending time on social media and other related activities has a beneficial impact on the area of mental health. There are, however, a lot of variances because of the cross-sectional design and sampling's technical restrictions. Through qualitative research and vertical cohort studies, the structure of social media affects on mental health has to be better analysed
"Analysing Mental Health Across Twitter Users by Sentiment Analysis", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.148 - 165, March-2023, Available :https://ijsdr.org/papers/IJSDR2303026.pdf
Volume 8
Issue 3,
March-2023
Pages : 148 - 165
Paper Reg. ID: IJSDR_204210
Published Paper Id: IJSDR2303026
Downloads: 000347223
Research Area: Computer Engineering
Country: Ghazipur, UTTAR PRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
