The Generalizability of Machine Learning Models of Personality across Two Text Domains

• Machine learning of high-dimensional models have received attention for their ability to predict psychological variables, such as personality. However, it has been less examined to what degree such models are capable of generalizing across domains. Across two text domains (Reddit message and personal essays), compared to low-dimensional- and theoretical models, atheoretical high-dimensional models provided superior predictive accuracy within but poor/non-significant predictive accuracy across domains. Thus, complex models depended more on the specifics of the trained domain. Further, when examining predictors of models, few survived across domains. We argue that theory remains important when conducting prediction-focused studies and that research on both high- and low-dimensional models benefit from establishing conditions under which they generalize.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

SAMHÄLLSVETENSKAP  -- Psykologi -- Psykologi (hsv//swe)

SOCIAL SCIENCES  -- Psychology -- Psychology (hsv//eng)

SAMHÄLLSVETENSKAP  -- Medie- och kommunikationsvetenskap -- Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning (hsv//swe)

SOCIAL SCIENCES  -- Media and Communications -- Information Systems, Social aspects (hsv//eng)

Nyckelord

machine learning

big five

LIWC

text analysis

Psychology

Psykologi

data- och systemvetenskap

Publikations- och innehållstyp

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