Abstract
The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or inappropriate sentiment labels. In this paper, we introduce SenWave, a novel fine-grained multi-language sentiment analysis dataset specifically designed for analyzing COVID-19 tweets, featuring ten sentiment categories across five languages. The dataset comprises 10,000 annotated tweets each in English and Arabic, along with 30,000 translated tweets in Spanish, French, and Italian, derived from English tweets. Additionally, it includes over 105 million unlabeled tweets collected during various COVID-19 waves. To enable accurate fine-grained sentiment classification, we fine-tuned pre-trained transformer-based language models using the labeled tweets. Our study provides an in-depth analysis of the evolving emotional landscape across languages, countries, and topics, revealing significant insights over time. Furthermore, we assess the compatibility of our dataset with ChatGPT, demonstrating its robustness and versatility in various applications. Our dataset and accompanying code are publicly accessible on the repository\footnote{this https URL}. We anticipate that this work will foster further exploration into fine-grained sentiment analysis for complex events within the NLP community, promoting more nuanced understanding and research innovations.
Abstract (translated)
COVID-19大流行对全球的影响凸显了全面理解公众情绪和反应的必要性。尽管有大量关于COVID-19的公共数据集可用，其中一些包含多达100亿个数据点，但仍有挑战存在，特别是在标记数据的可用性和粗粒度或不适当的感情标签方面。在本文中，我们介绍了SenWave，这是一个新颖的细粒度多语言情感分析数据集，专门用于分析COVID-19推文，涵盖了五种语言中的十个情绪类别。该数据集包括每种英语和阿拉伯语各10,000条标注推文，以及从英语推文中翻译出的30,000条西班牙语、法语和意大利语推文。此外，它还包括在各种COVID-19浪潮中收集到的大约1.05亿条未标记的推文。为了实现准确的细粒度情感分类，我们使用标注过的推文对预训练的基于转换器的语言模型进行了微调。我们的研究深入分析了跨语言、国家和地区的情绪演变情况，并随着时间的推移揭示了一些重要的见解。此外，我们还评估了我们的数据集与ChatGPT的兼容性，证明其在各种应用中的稳健性和多功能性。我们的数据集和配套代码可公开访问于该存储库\footnote{this https URL}。我们预计这项工作将促进自然语言处理社区对复杂事件中细粒度情感分析进一步探索，推动更细腻的理解和研究创新。
URL
https://arxiv.org/abs/2510.08214
