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

TL;DR

This paper introduces L3Cube-MahaSent-MD, a comprehensive multi-domain Marathi sentiment analysis dataset with around 60,000 samples across four domains, and evaluates transformer models for sentiment classification.

Contribution

It provides the first multi-domain Marathi sentiment dataset and benchmarks transformer models, highlighting the importance of low-resource multi-domain datasets.

Findings

MahaBERT achieved the highest accuracy among models.

Multi-domain datasets improve sentiment analysis in Marathi.

Cross-domain analysis reveals domain-specific challenges.

Abstract

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 .