  Psychometric measures of ability, attitudes, perceptions, and beliefs are
crucial for understanding user behaviors 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 afford opportunities for timely,
unobtrusive, collection and analysis. In this paper, we describe our efforts to
construct a corpus for psychometric natural language processing (NLP). 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 survey-based psychometric measures and accompanying user-generated
text from over 8,500 respondents. We report preliminary results on the use of
the text to categorize/predict users' survey response labels. We also discuss
the important implications of our work and resulting testbed for future
psychometric NLP research.
