Hope Speech Identification is the process of detecting positive, supportive, and encouraging language in text. It focuses on identifying content that promotes unity, inclusiveness, and resilience. Identification of hope speech helps supports mental well being, create healthier online environments, counter hate speech, and promote positive digital communication. This shared task hope speech detection in code-mixed Tulu language as part of DravidianLangTech @ ACL 2026, focuses on both the coarse-grained hope tone classification and the fine-grained hope type classification tasks. There are 11 teams participated in the tasks and submitted several runs for both the tasks. The teams are ranked based on the macro-F1 score.

The sentiment analysis in code-mixed Dravidian languages such as Tamil-English and Tulu-English is the focus of this study because these languages present difficulties for conventional techniques. In this work, We used ensembles, multilingual Bidirectional Encoder Representation(mBERT), Bidirectional Long Short Term Memory (BiLSTM), Random Forest (RF), Support Vector Machine (SVM), and preprocessing in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec feature extraction. mBERT obtained accuracy of 64% for Tamil and 68% for Tulu on development datasets. In test sets, the ensemble model gave Tamil a macro F1-score of 0.4117, while mBERT gave Tulu a macro F1-score of 0.5511. With regularization and data augmentation, these results demonstrate the approach’s potential for further advancements.