Biswas Susmoy, Zahid Md Mostafizur Rahman, Rabeya Mst Taposi, Abedin Md Minhazul, Bijoy Md Hasan Imam, Rahman Md Sadekur
Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Data Brief. 2025 Aug 6;62:111953. doi: 10.1016/j.dib.2025.111953. eCollection 2025 Oct.
Sarcasm is a form of sentiment often used for comedic effect. Its widespread use contributes to frequent misinterpretation of humour-based comments among native Bengali speakers. The growing prevalence of sarcasm in the Bengali language necessitates further study using natural language processing, as detecting Bengali sarcasm remains particularly challenging. To address this, the study introduces BanglaSarc3, a ternary-class dataset comprising 12,089 Facebook comments categorised as sarcastic with 4012 instances, neutral with 4056 instances, and non-sarcastic with 4021 instances. This dataset aims to tackle humour misinterpretation, which often leads to digital conflicts, providing a valuable resource for improving sarcasm detection in Bengali NLP research. This dataset serves as a benchmark for evaluating NLP models on Bengali sarcasm classification, fostering linguistic diversity and inclusive language models while ensuring balanced category representation. To enhance data quality, pre-processing steps such as anonymisation and duplicate removal were applied. Three native Bengali speakers independently assessed the text labels, ensuring reliability. Designed to advance NLP research, BanglaSarc3 supports applications in sarcasm detection, sarcastic text classification, language modelling, and education. By providing a robust foundation for temporal analysis in Bengali, it enhances the development of precise, context-aware NLP models. The dataset is openly available for academic and research purposes, promoting collaboration and innovation within the Bengali NLP community.
讽刺是一种常被用于喜剧效果的情感表达方式。它的广泛使用导致以孟加拉语为母语的人经常误解基于幽默的评论。孟加拉语中讽刺的使用日益普遍，因此有必要使用自然语言处理进行进一步研究，因为检测孟加拉语中的讽刺仍然特别具有挑战性。为了解决这个问题，该研究引入了BanglaSarc3，这是一个三元类数据集，包含12089条Facebook评论，其中4012条被分类为讽刺，4056条为中性，4021条为非讽刺。这个数据集旨在解决经常导致数字冲突的幽默误解问题，为改进孟加拉语自然语言处理研究中的讽刺检测提供宝贵资源。这个数据集作为评估孟加拉语讽刺分类自然语言处理模型的基准，促进语言多样性和包容性语言模型，同时确保类别表示的平衡。为了提高数据质量，应用了匿名化和重复删除等预处理步骤。三位以孟加拉语为母语的人独立评估了文本标签，确保了可靠性。BanglaSarc3旨在推进自然语言处理研究，支持在讽刺检测、讽刺文本分类、语言建模和教育等方面的应用。通过为孟加拉语的时间分析提供坚实基础，它促进了精确的、上下文感知自然语言处理模型的发展。该数据集可供学术和研究目的公开使用，促进孟加拉语自然语言处理社区内的合作与创新。
