@inproceedings{shashirekha-etal-2026-sjm,
    title = "{SJM}{\_}{MINDS}@{D}ravidian{L}ang{T}ech@{ACL}2026: Machine Learning Approaches for Hope Speech Detection in Code-Mixed {T}ulu",
    author = "Shashirekha, Hosahalli Lakshmaiah  and
      Manjula  and
      Krishna, Jayashree",
    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-1.56/",
    pages = "359--365",
    ISBN = "979-8-89176-401-9",
    abstract = "Hope speech detection is an important task in understanding emotionally constructive communication in online platforms, especially in low-resource and code-mixed languages. This paper describes our system submitted to the first shared task on Hope Speech Detection in Code-Mixed Tulu, organized by DravidianLangTech@ACL 2026. The shared task consists of two tasks: Task 1 - Coarse-Grained Hope Tone Classification and Task 2 - Fine-Grained Hope Type Classification, with the objective of detecting and classifying the tone and type of hope expressed in code-mixed Tulu texts. We experimented with Logistic Regression (LR) and Linear Support Vector Classifier (LinearSVC) - classical Machine Learning (ML) approaches, trained with Term Frequency and Inverse Document Frequency (TF-IDF) of word ngrams (n = 1, 2). For Task 1, we employed both models, whereas for Task 2, we employed only the LR model. Linear SVC obtained a macro F1-score of 0.51 in Task 1 and secured 4th rank, while the LR model obtained a macro F1-score of 0.37 in Task 2 and secured 5th rank. The results demonstrate that traditional ML approaches remain effective for low-resource code-mixed language scenarios."
}