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Hansi Hettiarachchi

Hansi Hettiarachchi has 4 papers in our library, in 2021–2025, most of them on Semitic languages, Sentiment & emotion, Evaluation & benchmarks; often with Tharindu Ranasinghe, Maram I. Alharbi, Ruslan Mitkov.

4 papers in our library · 2021–2025

OpenAlex · ORCID · h-index 13 · 58 works · 416 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 25 times in our library · ⭐ Paper Stars ranking: #9397

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

🎓Lancaster UniversityLancaster · United Kingdom2024–2026 · 18 papersEmployment since 2024 · Lecturer in Security and Protection Science, School of Computing and Communicationscurrent🎓Birmingham City UniversityBirmingham · United Kingdom2020–2024 · 25 papers🎓University of MoratuwaMoratuwa · Sri Lanka2016–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksSocial Sciences · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 workInformation Systems · 1 workSociology and Political Science · 1 work

🧭 Topics 4Research subjects and languages the work is about.Semitic languages · 4 worksSentiment & emotion · 3 worksEvaluation & benchmarks · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20253 papers20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Transformers to Fight the COVID-19 Infodemic (2021 · 16 citations)
    The paper describes the InfoMiner team's entry to the NLP4IF-2021 shared task on the COVID-19 infodemic. The task asked systems to label tweets in English, Arabic and Bulgarian with seven yes/no properties: whether a tweet holds a verifiable factual claim, seems false, matters to the public, is harmful, needs a…
  2. AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects (2025 · 15 citations)
    The AHaSIS 2025 shared task addresses a gap in Arabic sentiment analysis. Most labelled resources cover Modern Standard Arabic (MSA), and models trained on them transfer poorly to regional dialects. The organisers took MSA hotel reviews from the SemEval-2016 ABSA-Hotels data, collected from Booking.com and…
  3. ADOR: Dataset for Arabic Dialects in Hotel Reviews: A Human Benchmark for Sentiment Analysis (2025 · 6 citations)
    NLP tools for Arabic are trained mostly on Modern Standard Arabic (MSA), so regional dialects remain poorly served.
  4. Evaluating Large Language Models on Sentiment Analysis in Arabic Dialects (2025)
    The paper asks how well open large language models classify sentiment in dialectal Arabic, a setting with far fewer resources than Modern Standard Arabic (MSA).

 Co-authors here

5 co-authors with a page, the most papers together first.

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 25 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

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