How to predict creativity ratings from written narratives: A comparison of co-occurrence and textual forma mentis networks
It provides a practical workflow for researchers in cognitive fields like creativity research, offering guidance on when to use syntactic networks over surface models, but it is incremental as it compares existing methods on a specific dataset.
This paper compares two semantic network methods, word co-occurrence and textual forma mentis networks (TFMNs), for predicting human creativity ratings from short stories, finding that TFMNs consistently achieve lower prediction errors, with a best MAE of 0.581 compared to 0.592 for co-occurrence networks.
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.