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Transformer-based personality recognition from textual data

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dc.contributor.authorSan Juan, Brian Justin D.
dc.contributor.authorAgbuya, Jericho L.
dc.contributor.authorIlano, Alyssa Joyce D.
dc.contributor.authorLapore, Rose Charlene M.
dc.date.accessioned2025-10-08T03:49:37Z
dc.date.available2025-10-08T03:49:37Z
dc.date.issued2023-05
dc.descriptionFull text.
dc.description.abstractAutomatic personality trait recognition has become an area of growing interest across the fields ofpsychology, neuropsychology, and computer science. With the success of deep learning methods in various domains, researchers have increasingly employed deep neural networks to learn high-level feature representations for automatic personality trait recognition. In this study, a transformer-based personality recognition model is proposed. Specifically, the authors utilized James Pennebaker and Laura King's streamof-consciousness essay and employed various text processing techniques. The researchers trained various BERT-type models and found that the Distilbert-Base-Uncased transformer using R-Adam optimizer achieved the highest F-score of O. 76, outperforming other BERT-type models. Moreover, all proposed BERT-type models exhibited superior performance compared to existing works that have used the same corpus in personality trait recognition. The study demonstrates the effectiveness of transformer-based models
dc.identifier.citationSan Juan, B. J. D., Agbuya, J. L., Ilano, A. J. D., & Lapore, R. C. M. (2023) Transformer-based personality recognition from textual data [Unpublished Undergraduate thesis]. Don Mariano Marcos Memorial State University - South La Union Campus, Agoo, La Union.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/509
dc.language.isoen_US
dc.publisherDon Mariano Marcos Memorial State University - South La Union Campus
dc.sdgSDG 4
dc.titleTransformer-based personality recognition from textual data
dc.typeThesis
dspace.entity.typePublication
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