@inproceedings{kabir-etal-2025-advancing,
    title = "Advancing Subjectivity Detection in {B}engali News Articles Using Transformer Models with {POS}-Aware Features",
    author = "Kabir, Md Minhazul  and
      Ahmed, Kawsar  and
      Habib, Mohammad Ashfak  and
      Hoque, Mohammed Moshiul",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.11/",
    pages = "131--141",
    ISBN = "979-8-89176-314-2",
    abstract = "Distinguishing fact from opinion in text is a nuanced but essential task, particularly in news articles where subjectivity can influence interpretation and reception. Identifying whether content is subjective or objective is critical for sentiment analysis, media bias detection, and content moderation. However, progress in this area has been limited for low-resource languages such as Bengali due to a lack of benchmark datasets and tools. To address these constraints, this work presents BeNSD (Bengali News Subjectivity Detection), a novel dataset of 8,655 Bengali news article texts, along with an enhanced transformer-based architecture ($POS-Aware-MuRIL$) that integrates parts-of-speech (POS) features with MuRIL embeddings at the input level to provide richer contextual representation for subjectivity detection. A range of baseline models is evaluated, and the proposed architecture achieves a macro F1-score of 93.35{\%} in subjectivity detection for the Bengali language."
}