用简单界面让研究者零代码完成主题分析。
TALLMesh: a simple application for performing Thematic Analysis with Large Language Models
- 通过GUI和LLM自动生成文本编码与主题
- 支持人机协同迭代，无需编程基础
- 适合社科人文领域研究者快速上手
主题分析（Thematic Analysis, TA）是一种广泛用于识别和解释文本数据中模式的定性研究方法，例如在质性访谈中。近期研究表明，大型语言模型（LLMs）可有效辅助完成主题分析。本文提出一款名为TALLMesh的新应用，利用LLM协助研究人员开展主题分析。用户可通过图形化界面上传文本数据，系统自动生成初始编码与主题。整个过程基于Streamlit框架构建的GUI，配合Python脚本与LLM API实现。该界面特别适用于编程能力较弱的研究领域，如社会科学与人文学科。用户可采用“人在回路”方式，逐步优化编码与主题，无需编写代码或脚本。论文详细介绍了应用的核心功能，强调其在保持方法学严谨性的同时提升定性研究效率。同时讨论了应用设计与界面，展望了未来发展方向。
原文摘要 · Abstract (English)
Thematic analysis (TA) is a widely used qualitative research method for identifying and interpreting patterns within textual data, such as qualitative interviews. Recent research has shown that it is possible to satisfactorily perform TA using Large Language Models (LLMs). This paper presents a novel application using LLMs to assist researchers in conducting TA. The application enables users to upload textual data, generate initial codes and themes. All of this is possible through a simple Graphical User Interface, (GUI) based on the streamlit framework, working with python scripts for the analysis, and using Application Program Interfaces of LLMs. Having a GUI is particularly important for researchers in fields where coding skills may not be prevalent, such as social sciences or humanities. With the app, users can iteratively refine codes and themes adopting a human-in-the-loop process, without the need to work with programming and scripting. The paper describes the application key features, highlighting its potential for qualitative research while preserving methodological rigor. The paper discusses the design and interface of the app and outlines future directions for this work.
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