A deep learning architecture for psychometric natural language processing

Faizan Ahmad, Ahmed Abbasi, Jingjing Li, David G. Dobolyi, Richard G. Netemeyer, Gari D. Clifford, Hsinchun Chen

Research output: Contribution to journalArticle

Abstract

Psychometric measures reflecting people's knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.

Original languageEnglish (US)
Article number3365211
JournalACM Transactions on Information Systems
Volume38
Issue number1
DOIs
StatePublished - Feb 5 2020

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Keywords

  • Deep learning
  • Natural language processing
  • Psychometric measures
  • Text classification

ASJC Scopus subject areas

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications

Cite this

Ahmad, F., Abbasi, A., Li, J., Dobolyi, D. G., Netemeyer, R. G., Clifford, G. D., & Chen, H. (2020). A deep learning architecture for psychometric natural language processing. ACM Transactions on Information Systems, 38(1), [3365211]. https://doi.org/10.1145/3365211