@inproceedings{shashirekha-a-2026-mucs,
    title = "{MUCS}@Dravidianlangtech@{ACL}2026: Hope Speech Detection in Code-Mixed {T}ulu Language Using Multiple Features",
    author = "Shashirekha, Hosahalli Lakshmaiah  and
      A, Rachana",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.47/",
    pages = "306--315",
    ISBN = "979-8-89176-401-9",
    abstract = "Hope speech refers to online expressions that promote positivity, encouragement, and social harmony. It fosters inclusivity and resilience, making it particularly valuable in culturally diverse and code-mixed communities. Detecting hope speech is an emerging area in computational linguistics, aimed at supporting healthier digital interactions and improving accessibility for vulnerable groups.While most of the hope speech detection work has been focused on high-resource languages, low- resource languages such as Tulu remains unexplored. In this paper, we - Team MUCS, describe our proposed system submitted to the first shared task on Hope Speech Detection in Code-Mixed Tulu, organized by DravidianLangTech@ACL 2026. As there are no pretrained language models for Tulu, we explored multiple hand crafted features - word n-grams (n = 1, 3), character n-grams (n = 1, 3), syllable n-grams (n = 1, 3) and sub-words, to train ensemble of classical Machine Learning (ML) models: i) Multinomial Naive Bayes (MNB) and Logistic Regression (LR) classifiers and ii) k Nearest Neighbor (kNN) and Decision Tree (DT) classifiers, both with soft-voting. Experimental results demonstrate that feature integration effectively captures lexical, sub-lexical, and phonological cues in noisy code-mixed text. The system achieves competitive performance on both development and test datasets, highlighting the effectiveness of feature-based approaches for hope speech detection in code-mixed Tulu.An ablation study is also conducted to evaluate the contribution of multiple feature sets for hope speech detection."
}