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

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

Hadj Ahmed Bouarara
Copyright: © 2022 |Pages: 15
ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048
DOI: 10.4018/978-1-6684-6303-1.ch032
Cite Chapter Cite Chapter

MLA

Bouarara, Hadj Ahmed. "Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 581-595. https://doi.org/10.4018/978-1-6684-6303-1.ch032

APA

Bouarara, H. A. (2022). Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 581-595). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch032

Chicago

Bouarara, Hadj Ahmed. "Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 581-595. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch032

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.