Reading
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.
Section 01
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.
Section 02
Section 03
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.
Section 04
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.
Section 05
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.
Section 06
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.
Section 07
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.
Section 08
Considering research ethics and data protection regulations (e.g., GDPR), KWALLM has built-in multi-layer mechanisms for personal information identification and redaction:
This design ensures the privacy of research participants is protected while not compromising the quality of analysis.
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