  Given that natural language serves as the primary conduit for expressing
thoughts and emotions, text analysis has become a key technique in
psychological research. It enables the extraction of valuable insights from
natural language, facilitating endeavors like personality traits assessment,
mental health monitoring, and sentiment analysis in interpersonal
communications. In text analysis, existing studies often resort to either human
coding, which is time-consuming, using pre-built dictionaries, which often
fails to cover all possible scenarios, or training models from scratch, which
requires large amounts of labeled data. In this tutorial, we introduce the
pretrain-finetune paradigm. The pretrain-finetune paradigm represents a
transformative approach in text analysis and natural language processing. This
paradigm distinguishes itself through the use of large pretrained language
models, demonstrating remarkable efficiency in finetuning tasks, even with
limited training data. This efficiency is especially beneficial for research in
social sciences, where the number of annotated samples is often quite limited.
Our tutorial offers a comprehensive introduction to the pretrain-finetune
paradigm. We first delve into the fundamental concepts of pretraining and
finetuning, followed by practical exercises using real-world applications. We
demonstrate the application of the paradigm across various tasks, including
multi-class classification and regression. Emphasizing its efficacy and
user-friendliness, the tutorial aims to encourage broader adoption of this
paradigm. To this end, we have provided open access to all our code and
datasets. The tutorial is highly beneficial across various psychology
disciplines, providing a comprehensive guide to employing text analysis in
diverse research settings.
