Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews

• URL: http://arxiv.org/abs/2508.07517v1
• Date: Mon, 11 Aug 2025 00:27:52 GMT
• Title: Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews
• Authors: Joseph T. Colonel, Baihan Lin,
• Abstract summary: We introduce ThemeClouds, an open-source visualization tool that generates thematic, participant-weighted word clouds from dialogue transcripts.<n>The system prompts an LLM to identify concept-level themes across a corpus and then counts how many unique participants mention each topic.<n>Using interviews from a user study comparing five recording-device configurations, our approach surfaces more actionable device concerns than frequency clouds and topic-modeling baselines.
• Score: 13.971616443394474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Word clouds are a common way to summarize qualitative interviews, yet traditional frequency-based methods often fail in conversational contexts: they surface filler words, ignore paraphrase, and fragment semantically related ideas. This limits their usefulness in early-stage analysis, when researchers need fast, interpretable overviews of what participant actually said. We introduce ThemeClouds, an open-source visualization tool that uses large language models (LLMs) to generate thematic, participant-weighted word clouds from dialogue transcripts. The system prompts an LLM to identify concept-level themes across a corpus and then counts how many unique participants mention each topic, yielding a visualization grounded in breadth of mention rather than raw term frequency. Researchers can customize prompts and visualization parameters, providing transparency and control. Using interviews from a user study comparing five recording-device configurations (31 participants; 155 transcripts, Whisper ASR), our approach surfaces more actionable device concerns than frequency clouds and topic-modeling baselines (e.g., LDA, BERTopic). We discuss design trade-offs for integrating LLM assistance into qualitative workflows, implications for interpretability and researcher agency, and opportunities for interactive analyses such as per-condition contrasts (``diff clouds'').

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