Applying Text Mining to Analyze Human Question Asking in Creativity Research

TL;DR

This paper explores how text mining and natural language processing can be used to analyze questions in creativity research, revealing insights into question types, complexity, and their role in creative ideation.

Contribution

It introduces a novel text mining approach to measure the cognitive potential of questions and applies it to multiple datasets in creativity research.

Findings

Natural language processing can effectively analyze questions in creativity studies

Question type and complexity relate to creative problem solving

The proposed method provides new insights into the role of questions in creativity

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.