AutoTheme: A Multi-Agent Framework for Inductive Thematic Analysis with LLMs

Abstract

Thematic analysis is a qualitative research method used to identify and interpret patterns in textual data. However, it can be time-consuming and challenging to replicate. While recent advancements in large language models (LLMs) and generative AI have enhanced thematic analysis, existing methods often rely on prompt-based interactions and require significant human intervention. This paper introduces an agentic AI framework comprising autonomous, goal-directed agents powered by LLMs to perform inductive thematic analysis with minimal human input. We evaluate the framework using a dataset from a Cognitive Behavioral Therapy (CBT) mobile app and compare the results with those from Latent Dirichlet Allocation (LDA), demonstrating improved efficiency, adaptability, and thematic depth. Overall, the approach has shown efficacy, especially for short texts, such as app reviews and social media posts.