Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance.Traditionally, psychometric dimensions have been measured and collected using survey-based methods.Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis.In this work we construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain.We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed, which encompasses surveybased psychometric measures and accompanying user-generated text from 8,502 respondents.Our testbed also encompasses selfreported demographic information, including race, sex, age, income, and education, allowing for measuring bias and benchmarking fairness of text classification methods.We report preliminary results on use of the text to predict/categorize users' survey response labels and on the fairness of these models.We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.

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.