  In this work, we develop a pipeline for historical-psychological text
analysis in classical Chinese. Humans have produced texts in various languages
for thousands of years; however, most of the computational literature is
focused on contemporary languages and corpora. The emerging field of historical
psychology relies on computational techniques to extract aspects of psychology
from historical corpora using new methods developed in natural language
processing (NLP). The present pipeline, called Contextualized Construct
Representations (CCR), combines expert knowledge in psychometrics (i.e.,
psychological surveys) with text representations generated via
transformer-based language models to measure psychological constructs such as
traditionalism, norm strength, and collectivism in classical Chinese corpora.
Considering the scarcity of available data, we propose an indirect supervised
contrastive learning approach and build the first Chinese historical psychology
corpus (C-HI-PSY) to fine-tune pre-trained models. We evaluate the pipeline to
demonstrate its superior performance compared with other approaches. The CCR
method outperforms word-embedding-based approaches across all of our tasks and
exceeds prompting with GPT-4 in most tasks. Finally, we benchmark the pipeline
against objective, external data to further verify its validity.
