Stress detection from Twitter posts using LDA. (12th January 2021)

• Record Type: Journal Article
• Authors: Khan, Aysha; Ali, Rashid
• Is Part Of: International journal of high performance computing and networking. Volume 16:Number 2/3(2020)
• Page Start: 137
• Page End: 147
• Publication Date: 2021-01-12
• Subjects: stress detection -- Twitter posts -- machine learning -- sentiment analysis -- topic modelling -- latent Dirichlet allocation -- LDA; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

Psychological stress detection continues to remain a large problem among individuals. Identifying and combating stress before letting it take the face of some severe problems is of utmost importance. Traditional psychological stress detection techniques need professional devices and specialists to analyse the data, so it is very important that a method has to be introduced in which one can automatically detect the stress state of the user. In this work, we have made an effort to detect stress from the tweets of the users. We have collected different stressed and non-stressed related tweets from Twitter. Then, we have applied latent Dirichlet allocation (LDA), a popular machine learning algorithm, to detect stress among the individuals from their tweets and categorised the tweets into two classes stressed and non-stressed. We have also found experimentally that our LDA-based system performs better than the SVM-based system.