Efficient Keypoint Generation from Unstructured Qualitative Data via Large Language Models

Abstract

The exponential growth of unstructured qualitative data across domains—ranging from customer feedback and interviews to social media posts and open-ended survey responses—poses significant challenges for data-driven decision-making. Traditional methods for qualitative analysis often rely on manual coding, thematic categorization, or statistical text mining, which are time-consuming and lack contextual precision. Recent advancements in Large Language Models (LLMs) such as GPT-4 and Gemini have introduced new possibilities for efficiently extracting meaningful keypoints from massive text datasets. This research investigates an LLM-driven framework for keypoint generation, focusing on optimizing interpretability, accuracy, and efficiency in processing unstructured qualitative information. Using a comparative evaluation between transformer-based architectures and fine-tuned LLMs, this study demonstrates that LLM-assisted keypoint extraction not only enhances contextual relevance but also achieves up to 87% semantic fidelity compared to human-coded benchmarks. Experimental results indicate that integrating prompt optimization and self-reflective reasoning improves extraction quality while maintaining computational efficiency. The study concludes with an in-depth discussion of the implications for research, business intelligence, and knowledge management systems, paving the way for scalable qualitative analysis through AI-driven automation.