Comparative Analysis of NLP Model for Detecting Depression on Twitter

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dc.contributor.authorGupta, Khushi
dc.contributor.authorJinad, Razaq
dc.contributor.authorLiu, Qingzhong
dc.date.accessioned2023-11-21T15:24:06Z
dc.date.available2023-11-21T15:24:06Z
dc.date.issued2023-10-15
dc.description.abstractDepression is a serious mental health issue affecting a significant portion of the world’s population. With the widespread use of social media platforms, researchers have explored the possibility of utilizing natural language processing (NLP) techniques to detect signs of depression in users’ posts. In this paper, we present a comparative analysis of six different NLP models, namely BERT, RoBERTa, DistilBERT, ALBERT, Electra, and XLNet, for depression detection on Twitter data. The experiments compare the performance of different models, and the results reveal that the highest-performing models include XLNet, DistilBERT, and RoBERTa with accuracies of over 99%.
dc.identifier.citationGupta, K., Jinad, R., & Liu, Q. (2023) Comparative Analysis of NLP Model for Detecting Depression on Twitter. (Report No. IHS/CR-2023-1011). The Sam Houston State University Institute for Homeland Security.
dc.identifier.urihttps://hdl.handle.net/20.500.11875/4263
dc.language.isoen_US
dc.publisherInstitute for Homeland Security
dc.relation.ispartofseriesIHS; CR-2023-1011
dc.subjectNatural language processing, Depression detection, Transformers, Machine learning, Comparative analysis
dc.titleComparative Analysis of NLP Model for Detecting Depression on Twitter
dc.typeArticle

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