{
  "abstract": "Multi-domain sentiment classifiers may return a polarity label even when the evidence behind that decision becomes fragile under domain shift. This study evaluates an uncertainty-aware hybrid symbolic-lexicon framework designed to pair each polarity prediction with a traceable accept-or-defer status. The framework combines a lexical branch adapted only on training data with training-only fitted symbolic feature states, variable-precision rough evidence, and nested score fusion. Evaluation uses the UCI Sentiment Labelled Sentences dataset, comprising 3,000 balanced English sentences from Amazon Cells, IMDb, and Yelp, under repeated within-source, pooled multi-domain, and leave-one-source-out protocols. In pooled evaluation, the framework achieved 83.73% accuracy and 83.76% F1-score, improving F1 by 0.50 points over the adaptive lexical branch. The boundary mechanism deferred 53.55% of cases and retained 92.54% accuracy among accepted predictions; however, matched-coverage logistic regression and SVM confidence rankings produced lower selective risk. The contribution is therefore a reproducible, algorithmically traceable polarity-and-deferment mechanism rather than a claim of universal predictive or uncertainty-ranking superiority.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Saleh Yaakub"
    },
    {
      "name": "Mustafa Mat Deris"
    },
    {
      "name": "Ika Safitri Windiarti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d73ea9kjj4ahv.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1872",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1872",
  "id": "d73ea9kjj4ahv",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1872",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1702|Sentiment Analysis and Opinion Mining"
    ]
  ],
  "openalex": "W7223269718",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1872/1065",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/d73ea9kjj4ahv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:45Z",
      "native_id": "5bef5a87a9eb4b1d86f58bcdf330c8f2",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1872"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "An Uncertainty-Aware Hybrid Symbolic-Lexicon Framework for Multi-Domain Sentiment Classification",
  "title_text": "An Uncertainty-Aware Hybrid Symbolic-Lexicon Framework for Multi-Domain Sentiment Classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:45Z",
  "url": "https://research.draconitic.com/paper/d73ea9kjj4ahv",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}