{
  "annotation_id": "52a5d36e-8528-4768-9b87-a5c71afee643",
  "date_created": "2026-02-17T05:53:12.657000Z",
  "date_modified": "2026-02-17T05:53:12.657000Z",
  "file_hash": "e5711a69ef55f9a553020079c1363b76715fa213ce48fdd6dfd809d46ce176bb",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.07327",
    "authors": [
      "Roberto Passaro",
      "Edith Haim",
      "Massimo Stella"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "How to predict creativity ratings from written narratives: A comparison of co-occurrence and textual forma mentis networks",
    "url": "https://arxiv.org/abs/2601.07327",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a557976d-cdc3-4841-98ea-39d163533223",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}