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

Yuan, Lingyao
Zhang, Wenli
Scheibe, Kevin
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Personality 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.
Data, Text, and Web Mining for Business Analytics, natural language processing, persoanlity, sensory processing sensitivity
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