# L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models

arXiv: https://arxiv.org/abs/2306.13888 · HTML: https://mlsanity.com/paper/2306.13888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models](https://mlsanity.com/paper/2203.13778/) (2022)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021)
- [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022)

## Built on by (most cited first, 6 of 6)

- [On Limitations of LLM as Annotator for Low Resource Languages](https://mlsanity.com/paper/2411.17637/) (2024)
- [GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge](https://mlsanity.com/paper/2409.18193/) (2024)
- [Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages](https://mlsanity.com/paper/2409.04512/) (2024)
- [mahaNLP: A Marathi Natural Language Processing Library](https://mlsanity.com/paper/2311.02579/) (2023)
- [Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages](https://mlsanity.com/paper/2502.10140/) (2025)
- [L3Cube-MahaEmotions: A Marathi Emotion Recognition Dataset with Synthetic Annotations using CoTR prompting and Large Language Models](https://mlsanity.com/paper/2506.00863/) (2025)

## Abstract (arXiv)

The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles, and political tweets. The dataset consists of around 60,000 manually tagged samples covering 3 distinct sentiments - positive, negative, and neutral. We create a sub-dataset for each domain comprising 15k samples. The MahaSent-MD is the first comprehensive multi-domain sentiment analysis dataset within the Indic sentiment landscape. We fine-tune different monolingual and multilingual BERT models on these datasets and report the best accuracy with the MahaBERT model. We also present an extensive in-domain and cross-domain analysis thus highlighting the need for low-resource multi-domain datasets. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .