L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset
This addresses the problem of limited resources for sentiment analysis in Marathi, benefiting NLP researchers and practitioners focused on low-resource languages, though it is incremental as it follows similar efforts for other languages.
The authors tackled the lack of a proper sentiment analysis dataset for Marathi, a widely spoken Indian language, by creating L3CubeMahaSent, a publicly available dataset of ~16,000 tweets classified into positive, negative, and neutral categories, and provided baseline results using deep learning models.
Sentiment analysis is one of the most fundamental tasks in Natural Language Processing. Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such as Hindi, Bengali, Tamil have seen a significant amount of work in this area. However, the Marathi language which is the third most popular language in India still lags behind due to the absence of proper datasets. In this paper, we present the first major publicly available Marathi Sentiment Analysis Dataset - L3CubeMahaSent. It is curated using tweets extracted from various Maharashtrian personalities' Twitter accounts. Our dataset consists of ~16,000 distinct tweets classified in three broad classes viz. positive, negative, and neutral. We also present the guidelines using which we annotated the tweets. Finally, we present the statistics of our dataset and baseline classification results using CNN, LSTM, ULMFiT, and BERT-based deep learning models.