Tuesday 25 November 2025
A new benchmark dataset for sentiment analysis in Maithili, a low-resource language spoken by millions of people in India, has been created by researchers. This achievement is significant because it will enable more accurate and interpretable sentiment analysis models to be developed for this underserved language.
Maithili is an Indo-Aryan language that is rich in linguistic structure and cultural significance. Despite its importance, resources for Maithili are scarce, often limited to coarse-grained annotations and lacking interpretability mechanisms. This has hindered the development of natural language processing (NLP) models for Maithili, which has significant implications for applications such as sentiment analysis, text classification, and machine translation.
The new dataset, called SentiMaithili, consists of 3,221 annotated sentences that express different sentiments, including positive, negative, and neutral emotions. Each sentence is accompanied by a natural language justification written in Maithili, which provides contextual information about the sentiment expressed. This unique feature enables the development of more accurate and interpretable sentiment analysis models.
To create SentiMaithili, researchers carefully curated and validated the dataset using linguistic experts to ensure both label reliability and contextual fidelity. The dataset was also designed to promote culturally grounded interpretation and enhance the explainability of sentiment models.
The significance of SentiMaithili extends beyond Maithili itself. It sets a benchmark for explainable affective computing in low-resource languages, paving the way for more accurate and interpretable NLP models to be developed for other underserved languages. This achievement has far-reaching implications for applications such as text classification, machine translation, and human-computer interaction.
The development of SentiMaithili demonstrates the potential of collaborative efforts between researchers, linguists, and experts in NLP to create valuable resources for low-resource languages. As the demand for language models continues to grow, the creation of high-quality datasets like SentiMaithili will be essential for developing accurate and interpretable models that can benefit underserved communities.
The release of SentiMaithili marks an important milestone in the development of NLP for Maithili and has significant implications for the broader advancement of multilingual NLP and explainable AI. It highlights the importance of investing in low-resource languages and creating resources that are tailored to their unique linguistic and cultural contexts.
Cite this article: “Maithili Sentiment Analysis Benchmark Dataset: Enabling Accurate and Interpretable Models for Low-Resource Language”, The Science Archive, 2025.
Maithili, Sentiment Analysis, Natural Language Processing, Nlp, Low-Resource Languages, Linguistics, Cultural Significance, Annotation, Explainability, Affective Computing
