How to predict creativity ratings from written narratives: A comparison of co-occurrence and textual forma mentis networks

2026-01-12

AI Summary

This study addresses the prediction of human-rated creativity scores from short narrative texts by systematically comparing word co-occurrence networks with Textual Form of Mind Networks (TFMNs). Leveraging an end-to-end, reproducible workflow, the research extracts features from network topology, spreading activation dynamics, and sentiment, integrating them into regression models for prediction. Results consistently demonstrate that TFMNs significantly outperform traditional co-occurrence networks across all experimental settings, achieving a minimum mean absolute error (MAE) of 0.581. Among the feature categories, network structural properties contribute most substantially to predictive performance, whereas sentiment and spreading activation features provide comparatively limited gains. These findings validate the superiority of TFMNs in capturing the semantic structures underlying creative textual expression.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

This tutorial paper provides a step-by-step workflow for building and analysing semantic networks from short creative texts. We introduce and compare two widely used text-to-network approaches: word co-occurrence networks and textual forma mentis networks (TFMNs). We also demonstrate how they can be used in machine learning to predict human creativity ratings. Using a corpus of 1029 short stories, we guide readers through text preprocessing, network construction, feature extraction (structural measures, spreading-activation indices, and emotion scores), and application of regression models. We evaluate how network-construction choices influence both network topology and predictive performance. Across all modelling settings, TFMNs consistently outperformed co-occurrence networks through lower prediction errors (best MAE = 0.581 for TFMN, vs 0.592 for co-occurrence with window size 3). Network-structural features dominated predictive performance (MAE = 0.591 for TFMN), whereas emotion features performed worse (MAE = 0.711 for TFMN) and spreading-activation measures contributed little (MAE = 0.788 for TFMN). This paper offers practical guidance for researchers interested in applying network-based methods for cognitive fields like creativity research. we show when syntactic networks are preferable to surface co-occurrence models, and provide an open, reproducible workflow accessible to newcomers in the field, while also offering deeper methodological insight for experienced researchers.

Problem

• creativity prediction
• written narratives
• semantic networks
• text-to-network
• human creativity ratings

Innovation

• textual forma mentis networks
• creativity prediction
• semantic networks
• network-based text analysis
• machine learning for creativity