Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

Inamdar, S; Chapekar, R; Gite, S; Pradhan, B

Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

Inamdar, S Chapekar, R Gite, S Pradhan, B

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Human-Centric Intelligent Systems, 2023, 3, (2), pp. 80-91
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Inamdar, S
dc.contributor.author	Chapekar, R
dc.contributor.author	Gite, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2024-04-12T00:54:37Z
dc.date.available	2024-04-12T00:54:37Z
dc.date.issued	2023-06-01
dc.identifier.citation	Human-Centric Intelligent Systems, 2023, 3, (2), pp. 80-91
dc.identifier.issn	2667-1336
dc.identifier.issn	2667-1336
dc.identifier.uri	http://hdl.handle.net/10453/177788
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p> People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.</jats:p>
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Human-Centric Intelligent Systems
dc.relation.isbasedon	10.1007/s44230-023-00020-8
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-12T00:54:35Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	3
utslib.citation.issue	2

Abstract:

Abstract People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.

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http://hdl.handle.net/10453/177788