Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches

Abstract:For languages with limited resources, Sentiment Analysis remains a challenging problem due to limited annotated data and strong domain variability across textual sources. Marathi, despite being widely spoken, lacks extensive multi-domain sentiment analysis studies. Recent efforts by L3Cube have introduced multi-domain datasets in Marathi, enabling a better study of domain-aware sentiment classification. This study uses the pretrained MahaBERT model to improve sentiment classification for Marathi across different domains. Models are trained on individual domain datasets and domain-adaptive pretraining is applied using masked language modeling to help the model better understand domain-specific language. In addition, back-translation is used as a method of data augmentation to increase the diversity of training data. Results show that these methods lead to consistent improvements in accuracy across all domains. The improvements range from a minimum of about 0.12% to a maximum of about 1.74%, depending on the dataset. These results demonstrate that domain-adaptive pretraining and data augmentation are effective strategies for improving sentiment analysis models. This helps achieve more robust performance across different domains and makes the approach more suitable for low-resource languages like Marathi.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2610.15761 [cs.CL]

(or arXiv:2610.15761v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.15761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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