Sentiment Classification for Low-Resource Indian Languages Using Multilingual Transformers and Parameter-Efficient Fine-Tuning

NAMIT KHANDUJA; Nishant Kumar; Arun Chauhan

doi:10.64643/ijirtv12i10-195558-459

March 27, 2026International Journal of Innovative Research in TechnologyOpen Access

Sentiment Classification for Low-Resource Indian Languages Using Multilingual Transformers and Parameter-Efficient Fine-Tuning

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Key Points

The aim is to assess effective sentiment classification methods for low-resource Indian languages utilizing advanced multilingual models.
Evaluated the performance of multilingual transformers in sentiment classification.
Applied parameter-efficient fine-tuning techniques to enhance model training.
Focused on low-resource Indian languages for analysis.
Demonstrated improved sentiment classification accuracy for low-resource languages.
Highlighted the effectiveness of parameter-efficient fine-tuning in achieving better performance.

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Implication

Evaluates sentiment classification techniques for low-resource Indian languages, indicating innovative approaches for language processing.

Abstract

Explore the article titled Sentiment Classification for Low-Resource Indian Languages Using Multilingual Transformers and Parameter-Efficient Fine-Tuning from IJIRT Volume 12, Issue 10. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

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Cite This Study

KHANDUJA et al. (2026) studied this question.

synapsesocial.com/papers/69ccb79916edfba7beb899aa https://doi.org/10.64643/ijirtv12i10-195558-459

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Sentiment Classification for Low-Resource Indian Languages Using Multilingual Transformers and Parameter-Efficient Fine-Tuning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Sentiment Classification for Low-Resource Indian Languages Using Multilingual Transformers and Parameter-Efficient Fine-Tuning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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