Sarcasm detection involves predicting whether a given text is sarcastic, a challenging task in sentiment analysis. While significant research has been conducted for languages like English, Czech, and Italian, limited work exists for Indian languages such as Hindi, Tamil, and Bengali. Marathi, being the third most spoken language in India, has seen little progress in sarcasm detection, mainly due to the lack of suitable datasets.
Please cite the following paper when using this dataset:
Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025 (Paper accepted for publication, Preprint: https://arxiv.org/abs/2412.17180).
Abstract:
To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598
Please cite the following paper when using this dataset:
N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292
Abstract
Chinese electric power audit text dataset
