Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features
Journal
2020 IEEE International Conference on Data Mining (ICDM)
Type
journal article
Date Issued
2020
Author(s)
Abstract (De)
State-of-the-art personality prediction with text data mostly relies on bottom up, automated feature generation as part of the deep learning process. More traditional models rely on hand-crafted, theory-based text-feature categories. We propose a novel deep learning-based model which integrates traditional psycholinguistic features with language model embeddings to predict personality from the Essays dataset for Big-Five and Kaggle dataset for MBTI. With this approach we achieve state-of-the-art model performance. Additionally, we use interpretable machine learning to visualize and quantify the impact of various language features in the respective personality prediction models. We conclude with a discussion on the potential this work has for computational modeling and psychological science alike.
Language
English
Refereed
Yes
Publisher
IEEE
Start page
1184
End page
1189
Division(s)
Eprints ID
264570
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Bottom-Up_and_Top-Down_Predicting_Personality_with_Psycholinguistic_and_Language_Model_Features.pdf
Size
570.76 KB
Format
Adobe PDF
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