EconPapers    
Economics at your fingertips  
 

Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

Hadj Ahmed Bouarara
Additional contact information
Hadj Ahmed Bouarara: GeCoDe Laboratory, Algeria

International Journal of Information Systems and Social Change (IJISSC), 2021, vol. 12, issue 2, 1-15

Abstract: A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behavior in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia, etc. For this, the author used text mining and machine learning algorithms (naïve Bayes, k-nearest neighbours) to analyse tweets. The obtained results were validated using different evaluation measures such as f-measure, recall, precision, entropy, etc.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJISSC.2021040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jissc0:v:12:y:2021:i:2:p:1-15

Access Statistics for this article

International Journal of Information Systems and Social Change (IJISSC) is currently edited by John Wang

More articles in International Journal of Information Systems and Social Change (IJISSC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

 
Page updated 2025-03-19
Handle: RePEc:igg:jissc0:v:12:y:2021:i:2:p:1-15