L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset
  and Transformer Models
Aabha Pingle Aditya Vyawahare Isha Joshi Rahul Tangsali Raviraj Joshi
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 .