GPT is an effective tool for multilingual psychological text analysis
By Steve Rathje, Dan-Mircea Mirea, Ilia Sucholutsky, Raja Marjieh, Claire E. Robertson, Jay J. Van Bavel. Proceedings of the National Academy of Sciences of the United States of America, 2024
369 citations recorded by the source corpus.
Significance Many fields—including psychology, sociology, communications, political science, and computer science—use computational methods to analyze text data. However, existing text analysis methods have a number of shortcomings. Dictionary methods, while easy to use, are often not very accurate when compared to recent methods. Machine learning models, while more accurate, can be difficult to train and use. We demonstrate that the large-language model GPT is capable of accurately detecting various psychological constructs (as judged by manual annotators) in text across 12 languages, using simple prompts and no additional training data. GPT thus overcomes the limitations present in existing methods. GPT is also effective in several lesser-spoken languages, which could facilitate text analysis research from understudied contexts.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/89ec06ea4230e1c7ba1cb3dc8eaceef4472f5d9c
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Citations in the corpus
- Large Language Models Can Be Used to Estimate the Latent Positions of Politicians (2023, background): “For example, these works have studied how generative LLMs can be used for labeling purposes [29], analyzing text along psychological constructs [30], reducing the divisiveness of online conversations [31], and generating artificially politically extreme responses [32].”