词云作为共同声音：LLM辅助的定性访谈中参与者加权主题可视化
Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews
AI总结：
提出开源工具ThemeClouds，利用LLM从访谈转录文本中生成基于参与者提及广度的主题加权词云，相比频率词云和主题建模基线能更有效地浮现可操作见解。
摘要：
词云是总结定性访谈的常见方式，但传统基于频率的方法在对话语境中常失效：它们会浮现填充词，忽略改写，并割裂语义相关的想法。这限制了其在早期分析中的实用性，而此时研究人员正需要对参与者实际所说内容进行快速、可解释的概览。我们引入了ThemeClouds，一个开源可视化工具，利用大型语言模型（LLMs）从对话转录文本中生成主题性、参与者加权的词云。该系统提示LLM在整个语料库中识别概念层面的主题，随后统计有多少名不同的参与者提及每个主题，从而生成基于提及广度而非原始词频的可视化。研究人员可自定义提示词和可视化参数，提供了透明度与控制力。使用一项用户研究的访谈（比较五种录音设备配置；31名参与者；155份转录文本，Whisper ASR），我们的方法比频率词云和主题建模基线（如LDA、BERTopic）浮现出更多可操作的设备问题。我们讨论了将LLM辅助整合到定性工作流程中的设计权衡、对可解释性和研究者自主性的影响，以及交互式分析的机会，如按条件对比（“差异词云”）。
英文摘要：
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'').
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
