Yuqi ChenPeking Universitycyq0722@pku.edu.cnSixuan LiXiaoying AI Lablisixuan@xiaoyingai.comYing LiPeking Universityyingliclaire@pku.edu.cnMohammad AtariUniversity of Massachusetts Amherstmatari@umass.eduAbstractIn 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 psychologyrelies 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 measurepsychological constructs such as traditionalism, norm strength, and collectivism in classicalChinese corpora. Considering the scarcity of available data, we propose an indirect supervisedcontrastive 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 superiorperformance 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.