  Sentiment analysis plays a crucial role in understanding the sentiment
expressed in text data. While sentiment analysis research has been extensively
conducted in English and other Western languages, there exists a significant
gap in research efforts for sentiment analysis in low-resource languages.
Limited resources, including datasets and NLP research, hinder the progress in
this area. In this work, we present an exhaustive study of data augmentation
approaches for the low-resource Indic language Marathi. Although
domain-specific datasets for sentiment analysis in Marathi exist, they often
fall short when applied to generalized and variable-length inputs. To address
this challenge, this research paper proposes four data augmentation techniques
for sentiment analysis in Marathi. The paper focuses on augmenting existing
datasets to compensate for the lack of sufficient resources. The primary
objective is to enhance sentiment analysis model performance in both in-domain
and cross-domain scenarios by leveraging data augmentation strategies. The data
augmentation approaches proposed showed a significant performance improvement
for cross-domain accuracies. The augmentation methods include paraphrasing,
back-translation; BERT-based random token replacement, named entity
replacement, and pseudo-label generation; GPT-based text and label generation.
Furthermore, these techniques can be extended to other low-resource languages
and for general text classification tasks.
