L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset

Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, Raviraj Joshi


Abstract
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 models.
Anthology ID:
2021.wassa-1.23
Volume:
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
April
Year:
2021
Address:
Online
Venues:
EACL | WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
213–220
Language:
URL:
https://aclanthology.org/2021.wassa-1.23
DOI:
Bibkey:
Cite (ACL):
Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, and Raviraj Joshi. 2021. L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 213–220, Online. Association for Computational Linguistics.
Cite (Informal):
L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset (Kulkarni et al., WASSA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wassa-1.23.pdf
Code
 l3cube-pune/MarathiNLP
Data
L3CubeMahaSent