Applying Text Mining to Analyze Human Question Asking in Creativity Research
This work addresses the need for empirical analysis of question asking in creativity research, but it is incremental as it applies existing text mining methods to this domain without major methodological breakthroughs.
This paper tackles the problem of understanding the role of questions in creativity by applying text mining methods to measure the cognitive potential of questions, analyzing factors like type, complexity, and answer content across five datasets, with results suggesting that natural language processing can contribute to creativity research.
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.