TALLMesh: a simple application for performing Thematic Analysis with Large Language Models

• URL: http://arxiv.org/abs/2504.13892v1
• Date: Sat, 05 Apr 2025 15:10:08 GMT
• Title: TALLMesh: a simple application for performing Thematic Analysis with Large Language Models
• Authors: Stefano De Paoli, Alex Fawzi,
• Abstract summary: Thematic analysis (TA) is a widely used qualitative research method for identifying and interpreting patterns within textual data.<n>Recent research has shown that it is possible to satisfactorily perform TA using Large Language Models (LLMs)<n>This paper presents a novel application using LLMs to assist researchers in conducting TA.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code [0.0]
  Large language models (LLMs) have demonstrated remarkable program comprehension capabilities.<n> transformer-based topic modeling techniques offer effective ways to extract semantic information from text.<n>This paper proposes and explores a novel approach that combines these strengths to automatically identify meaningful topics in a corpus of Python programs.
  arXiv  Detail & Related papers  (2025-04-24T10:30:40Z)
• Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol [83.90769864167301]
  Literature review tables are essential for summarizing and comparing collections of scientific papers.<n>We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers.<n>Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques.
  arXiv  Detail & Related papers  (2025-04-14T14:52:28Z)
• Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
  Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities.<n>This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
  arXiv  Detail & Related papers  (2024-12-03T03:59:03Z)
• Large Language Model-Brained GUI Agents: A Survey [42.82362907348966]
  multimodal models have ushered in a new era of GUI automation.<n>They have demonstrated exceptional capabilities in natural language understanding, code generation, and visual processing.<n>These agents represent a paradigm shift, enabling users to perform intricate, multi-step tasks through simple conversational commands.
  arXiv  Detail & Related papers  (2024-11-27T12:13:39Z)
• GUI Agents with Foundation Models: A Comprehensive Survey [91.97447457550703]
  This survey consolidates recent research on (M)LLM-based GUI agents.<n>We identify key challenges and propose future research directions.<n>We hope this survey will inspire further advancements in the field of (M)LLM-based GUI agents.
  arXiv  Detail & Related papers  (2024-11-07T17:28:10Z)
• Thematic Analysis with Open-Source Generative AI and Machine Learning: A New Method for Inductive Qualitative Codebook Development [0.0]
  We present the Generative AI-enabled Theme Organization and Structuring (GATOS) workflow. It uses open-source machine learning techniques, natural language processing tools, and generative text models to facilitate thematic analysis. We show that the GATOS workflow is able to identify themes in the text that were used to generate the original synthetic datasets.
  arXiv  Detail & Related papers  (2024-09-28T18:52:16Z)
• Automating the Information Extraction from Semi-Structured Interview Transcripts [0.0]
  This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. We present a user-friendly software prototype that enables researchers to efficiently process and visualize the thematic structure of interview data.
  arXiv  Detail & Related papers  (2024-03-07T13:53:03Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions [114.67699010359637]
  We analyze a large-scale collection of real user queries to GPT. We find that tasks such as design'' and planning'' are prevalent in user interactions but are largely neglected or different from traditional NLP benchmarks.
  arXiv  Detail & Related papers  (2023-10-19T02:12:17Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Text Mining for Processing Interview Data in Computational Social Science [0.6820436130599382]
  We use commercially available text analysis technology to process interview text data from a computational social science study. We find that topical clustering and terminological enrichment provide for convenient exploration and quantification of the responses. We encourage studies in social science to use text analysis, especially for exploratory open-ended studies.
  arXiv  Detail & Related papers  (2020-11-28T00:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.