A novel approach to cross-linguistic transfer learning for hope speech detection in Tamil and Malayalam

In the field of Natural Language Processing (NLP), accurately identifying hope speech in low-resource languages such as Tamil and Malayalam poses significant challenges. This research introduces the Sentimix Transformer (SentT), a novel transformer-based model designed for detecting hope speech in YouTube comments composed in Tamil and Malayalam, two linguistically rich but computationally low-resource languages. The SentT model innovatively combines multilingual BERT (mBERT) embeddings with specialized cultural and code-mixing adaptations to effectively process the linguistic diversity and complexities inherent in code-mixed data. This approach allows SentT to capture nuanced expressions of hope by integrating domain-specific knowledge into the transformer framework. Our methodology extends traditional transformer architectures by incorporating a unique ensemble of embeddings that encapsulate linguistic, cultural, and code-mixing attributes, significantly enhancing the model’s sensitivity to context and cultural idioms. We validate our approach using the Hope Speech dataset for Equality, Diversity, and Inclusion (HopeEDI), which includes diverse comments from social media. The SentT model achieves an impressive accuracy of 93.4%, a precision of 92.7%, and a recall of 94.1% outperforming existing models and demonstrating its efficacy in handling the subtleties of hope speech in multilingual contexts. The model’s architecture and the results of extensive evaluations not only underscore its effectiveness but also its potential as a scalable solution for similar tasks in other low-resource languages. Through this research, we contribute to the broader field of sentiment analysis by demonstrating the potential of tailored, context-aware models in enhancing digital communication’s positivity and inclusiveness.