Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioural Scientists

Humans use language to communicate complex psychological experiences. Advancements in Artificial Intelligence (AI) have enabled researchers to quantitatively study and assess psychological constructs through language (e.g., well-being and mental health). Data-driven visualizations of how language statistically relates to psychological dimensions can help interpret assessment scores, uncover meaningful patterns, and understand the intricate relationships between language use and mental states. Visualizations can, for example, be used to differentiate between related constructs (e.g., How does language related to depression versus anxiety differ?), understand the validity of assessment tools (e.g., Does the language related to high depression severity match with depression theory?), and explain AI-based language assessments (e.g., Which language is indicative of higher depression severity as identified by an AI-based model?).This tutorial demonstrates how to transform language data into visual insights using the text and topics packages in R. We provide practical guidance on leveraging four different techniques, examining how language use patterns are statistically associated with a psychological construct or behavior. The techniques include visualizing 1) individual words and phrases, 2) topics (word clusters), 3) words based on their numerical representation in the AI-based language space, and 4) complete language examples. These quantitative methods offer complementary perspectives, visualizing linguistic elements from individual words to overarching topics and relevant text examples. The methods range from simple correlations between individual words to more advanced techniques, including Large Language Models and topic modelling. By making these methods accessible, this tutorial aims to empower researchers to use language visualizations for meaningful data-driven insights.