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Cheryl Seals

Cheryl Seals has 2 papers in our library, in 2022–2026, most of them on Sentiment & emotion, Semitic languages, English.

2 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 10 · 116 works · 580 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 3 times in our library · ⭐ Paper Stars ranking: #10265

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Auburn UniversityAuburn · United States2005–2026 · 95 paperscurrent🎓Virginia TechBlacksburg · United States1999–2004 · 8 papers🎓Grambling State UniversityGrambling · United States2020–2024 · 3 papers🎓North Carolina Agricultural and Technical State UniversityGreensboro · United States2020–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.English · 1 workSemitic languages · 1 workSentiment & emotion · 1 work

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning (2022 · 6 citations)
    The paper addresses SubTask A of SemEval-2022 Task 6 (iSarcasmEval): deciding whether a short social-media text is sarcastic, in English and Arabic. The labels come from the writers themselves. The authors argue that a contextual encoder alone poorly captures intent, so they add class-level style signals.
  2. Seals-NLP at SemEval-2026 Task 9: A Comparative Study of Transformer Architectures for Polarization Detection (2026 · 1 citation)
    The paper addresses binary polarization detection in English social-media posts (SemEval-2026 Task 9, POLAR, Subtask 1). It asks which modeling family handles this task best, using only openly available models. The authors compare three approaches:

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 3 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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