{
  "abstract": "Language use, vocal behavior, and facial activity provide complementary indicators of affective state, but multimodal models can be affected by noisy temporal observations and insufficient global guidance before cross-modal interaction. We developed a framework combining bidirectional gated recurrent unit encoders, attention pooling, dynamic hypernode injection, and graph attention fusion. Textual, acoustic, and visual sequences were mapped into a shared latent space and compressed into modality-level representations. A sample-dependent hypernode and a learnable static prior were then injected through gated residual connections before graph propagation. The model was evaluated on CMU-MOSI and CMU-MOSEI using five random seeds and validation-MAE checkpoint selection. On CMU-MOSI, the model obtained an MAE of 0.7290±0.0099, a correlation of 0.7880±0.0076, and an Acc-7 of 0.4561±0.0160. On CMU-MOSEI, the corresponding results were 0.5541±0.0053, 0.7551±0.0058, and 0.5269±0.0064. A five-run comparison of pre-GAT, post-GAT, and no injection showed that pre-GAT injection provided the strongest overall regression-oriented balance, although post-GAT was slightly higher on MOSEI Acc-7. Gate, graph-attention, representation, and balanced case diagnostics showed that the injected prior was sample dependent and modality selective. These findings support pre-propagation hypernode injection under complete, word-aligned tri-modal inputs, without establishing universal superiority or robustness to missing and corrupted modalities.",
  "abstract_shown": true,
  "affiliations": [
    "College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
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        0
      ],
      "name": "Wenfei Cao"
    },
    {
      "name": "Jian Xu"
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    {
      "name": "Yinghao Li"
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    {
      "name": "Huizi Yan"
    },
    {
      "name": "Zhuowei Hu"
    },
    {
      "name": "Wenxu Chen"
    }
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  "bibtex": "https://research.draconitic.com/paper/3wg6phdsm9xa4.bib",
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  "doi": "10.1371/journal.pone.0359425",
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  "id": "3wg6phdsm9xa4",
  "issns": [
    "1932-6203"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "1702|Sentiment Analysis and Opinion Mining"
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      "/Biology and life sciences/Psychology/Cognitive psychology/Language/Multilingualism"
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      "plos",
      "/Biology and life sciences/Psychology/Emotions"
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      "/Physical sciences/Physics/Acoustics/Acoustic signals"
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      "/Research and analysis methods/Database and informatics methods/Bioinformatics/Sequence analysis/Sequence alignment"
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      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
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      "/Social sciences/Psychology/Behavior/Verbal behavior/Verbal communication"
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      "/Social sciences/Psychology/Cognitive psychology/Language"
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    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Language/Multilingualism"
    ],
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      "plos",
      "/Social sciences/Psychology/Emotions"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359425&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/3wg6phdsm9xa4.ris",
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    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0359425",
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      "url": "https://doi.org/10.1371/journal.pone.0359425"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:bioinformatics",
      "subfield": "bioinformatics"
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    {
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      "origin": "plos:neuroscience",
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  "title": "Dynamic hypernode injection and graph attention fusion for multimodal sentiment analysis of behavioral signals",
  "title_text": "Dynamic hypernode injection and graph attention fusion for multimodal sentiment analysis of behavioral signals",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/3wg6phdsm9xa4",
  "venue": "PLOS One",
  "versions": []
}