Work

ThatiAR: Subjectivity Detection in Arabic News Sentences

Year: 2025

Type: conference-paper

Abstract: In this study, we present the first large dataset, ThatiAR, for subjectivity detection in Arabic, consisting of ~3.6K manually annotated sentences, and GPT-4o based explanations. In addition, we include instructions (both in English and Arabic) to facilitate LLM based fine-tuning. We provide an in-depth analysis of the dataset, annotation process, and extensive benchmark results, including PLMs and LLMs. Our analysis of the annotation process highlights that annotators were strongly influenced by their political, cultural, and religious backgrounds, especially at the beginning of the annotation process. The experimental results suggest that LLMs with in-context learning provide better performance. We release the dataset and resources to the community.

Source: Proceedings of the International AAAI Conference on Web and Social Media

DOI: 10.1609/icwsm.v19i1.35960

Authors: Reem Suwaileh, Maram Hasanain, Fatema Hubail, Wajdi Zaghouani, Firoj Alam

Institutions: Hamad bin Khalifa University, Freie Universität Berlin, Northwestern University in Qatar

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Fonds National de la Recherche Luxembourg, Qatar National Research Fund

Award: G2541145698