SJM_MINDS@DravidianLangTech@ACL2026: Machine Learning Approaches for Hope Speech Detection in Code-Mixed Tulu
Year: 2026
Type: conference-paper
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 Dravidian-LangTech@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 4 th rank, while the LR model obtained a macro F1-score of 0.37 in Task 2 and secured 5 th rank.The results demonstrate that traditional ML approaches remain effective for low-resource code-mixed language scenarios.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166831260