Title: Stress detection from Twitter posts using LDA

Authors: Aysha Khan; Rashid Ali

Addresses: Department of Computer Engineering, ZHCET, AMU, Aligarh, India ' Department of Computer Engineering, ZHCET, AMU, Aligarh, India

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.

Keywords: stress detection; Twitter posts; machine learning; sentiment analysis; topic modelling; latent Dirichlet allocation; LDA.

DOI: 10.1504/IJHPCN.2020.112700

International Journal of High Performance Computing and Networking, 2020 Vol.16 No.2/3, pp.137 - 147

Received: 10 Jul 2020
Accepted: 12 Sep 2020

Published online: 12 Jan 2021 *

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