# KWALLM: A Large Language Model-Based Qualitative Text Analysis Tool for Social Science Research

> KWALLM is a qualitative text analysis application developed using R and Shiny, enabling non-technical users to perform analysis tasks such as text classification, topic extraction, and sentiment scoring using large language models.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-06T20:45:42.000Z
- 最近活动: 2026-06-06T20:49:11.752Z
- 热度: 163.9
- 关键词: 质性研究, 文本分析, 大语言模型, R语言, Shiny, 社会科学, 主题建模, 人机协同, PII脱敏, 计算社会科学
- 页面链接: https://www.zingnex.cn/en/forum/thread/kwallm-10e514f3
- Canonical: https://www.zingnex.cn/forum/thread/kwallm-10e514f3
- Markdown 来源: floors_fallback

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## Introduction / Main Post: KWALLM: A Large Language Model-Based Qualitative Text Analysis Tool for Social Science Research

KWALLM is a qualitative text analysis application developed using R and Shiny, enabling non-technical users to perform analysis tasks such as text classification, topic extraction, and sentiment scoring using large language models.

## Original Author and Source

- **Original Author/Maintainer**: Kennispunt Twente
- **Source Platform**: GitHub
- **Original Project Name**: KWALLM
- **Original Link**: https://github.com/KennispuntTwente/KWALLM
- **Release Date**: June 2026

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## Project Overview

KWALLM is a text analysis application specifically designed for qualitative research, developed by Kennispunt Twente (Knowledge Center Twente, Netherlands). Built on the R language and Shiny framework, it encapsulates the powerful capabilities of large language models (LLMs) in a user-friendly web interface, allowing social science researchers to conduct efficient text analysis without a programming background.

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## Classification Analysis

Users can predefine a list of categories, and the model will automatically classify texts. For example, product reviews can be categorized into "positive", "negative", or "neutral". This supervised classification method is suitable for research scenarios with a clear analysis framework already in place.

## Feature Scoring

Users define specific features (e.g., "level of positive emotion"), and the model scores texts based on their matching degree with the feature. This method provides more fine-grained quantitative indicators than simple classification, making it suitable for research questions that require measuring degrees.

## Topic Extraction

Without predefined categories, the model automatically identifies topics in texts and assigns labels. This method is based on the research findings of Wanrooij, Manhar & Yang (2024) and Pham et al. (2023), and outperforms traditional methods like BERTopic on small datasets.

## Text Tagging

For qualitative coding needs, the model can mark text segments related to specific codes. For example, given the code "color", the model will highlight all text segments mentioning colors (such as "yellow" in "The sun is yellow"). Users can customize codes or let the LLM automatically generate codes based on the text. This mode is particularly suitable for analyzing long texts like interview records or focus group discussions.

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## Automatic PII Redaction

Considering research ethics and data protection regulations (e.g., GDPR), KWALLM has built-in multi-layer mechanisms for personal information identification and redaction:

- **Basic Detection**: Uses regular expressions to identify common PII such as email addresses, phone numbers, and Dutch postal codes
- **Advanced Detection**: Integrates the GLiNER model for localized deep PII identification without sending sensitive data to external APIs

This design ensures the privacy of research participants is protected while not compromising the quality of analysis.