The Devil is in The Details: Measuring Sensory Processing Sensitivity Using Natural Language Processing

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dc.contributor.authorYuan, Lingyao
dc.contributor.authorZhang, Wenli
dc.contributor.authorScheibe, Kevin
dc.date.accessioned2021-12-24T17:28:33Z
dc.date.available2021-12-24T17:28:33Z
dc.date.issued2022-01-04
dc.description.abstractPersonality traits play a strong role in our perceptions, attitudes, and decision-making behaviors in our daily lives, including our choices of words and writing patterns. While prior Information Systems (IS) research on personality typically used the Big Five personality traits as a theoretical framework, we look into measuring a comparatively new inherent personality trait, sensory processing sensitivity, using natural language processing. We collect data on twenty general essay questions from along with self-reported sensory processing sensitivity survey questions from 241 participants. We categorize participants based on survey questions with multiple methods and derive different features from the textual data. Our results show almost perfect agreement among the different methods categorizing a highly sensitive person versus a non-highly sensitive person. The initial analysis demonstrates that certain features can be of great potential in measuring sensory processing sensitivity in written text.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2022.164
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79496
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text, and Web Mining for Business Analytics
dc.subjectnatural language processing
dc.subjectpersoanlity
dc.subjectsensory processing sensitivity
dc.titleThe Devil is in The Details: Measuring Sensory Processing Sensitivity Using Natural Language Processing
dc.type.dcmitext

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