Sentiment Analysis for Low-Resource Languages: The Case of Nagamese
Published:Dec 1, 2025 04:01
•1 min read
•ArXiv
Analysis
This research addresses a critical issue in NLP: sentiment analysis for languages with limited data. The paper's contribution lies in applying machine learning to a low-resource language, expanding the scope of sentiment analysis applications.
Key Takeaways
- •Applies machine learning to analyze sentiment in Nagamese, a low-resource language.
- •Contributes to the broader field of NLP by addressing the challenge of limited data.
- •Potentially provides insights for similar projects involving other under-resourced languages.
Reference
“The study focuses on sentiment analysis and emotion classification using machine learning techniques.”