Applying Text Mining to Analyze Human Question Asking in Creativity Research

2025-01-03

AI Summary

The cognitive mechanisms by which questions stimulate creativity remain poorly understood, and the “question–idea” relationship lacks a computationally tractable characterization. Method: We propose the first cognitive-potential analysis framework for creativity assessment, introducing a multidimensional NLP paradigm that jointly models syntactic structure, semantic complexity, and answer-creativity mapping—integrating text classification, complexity modeling, semantic similarity computation, and cross-dataset validation. Contribution/Results: Evaluated systematically across five empirical datasets, our approach achieves strong discrimination between high- and low-creativity-potential questions (mean AUC = 0.89), with results demonstrating reproducibility and interpretability. This work establishes the first quantitative, computational model of question-induced creative potential, providing a standardized, theory-grounded analytical tool for investigating creative cognition mechanisms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Web Mining and Content Analysis: Community question answering
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research.

Problem

• Creativity
• Questioning
• Language Processing

Innovation

• Text Mining
• Language Processing
• Creativity Enhancement