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

Humans use language to communicate psychological experiences. Advancements in natural language processing have enabled researchers to quantitatively study and assess psychological states and behaviors through language. Insights from these language analyses are often best conveyed through visualizations. 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 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 language-based assessments (e.g., Which language is indicative of higher depression severity in a language-based assessment?). In this tutorial, we demonstrate how to transform language data into visual insights using the text and topics packages in R. We provide practical guidance on creating four types of visualizations based on different language-analysis techniques, examining how language-use patterns are statistically associated with psychological constructs. We visualize (a) individual words and phrases (n-grams), (b) topics (word clusters), (c) words in the word-embedding space, and (d) relevant language examples. The different visualizations offer complementary perspectives, visualizing linguistic elements from individual words to overarching topics and text examples. The underlying methods range from correlations between individual words to more advanced techniques, including topic modeling and large language models. By making these methods accessible, we aim to empower researchers to use language visualizations for meaningful, data-driven insights.