A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles

Please cite the following paper when using this dataset: N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A.Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” arXiv [cs.CY], 2024. Available: http://arxiv.org/abs/2406.07693 Abstract This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.使用本数据集时请引用以下论文：N. Thakur、V. Su、M. Shao、K. Patel、H. Jeong、V. Knieling与A. Bian：《针对YouTube、TikTok及其他平台上2024年麻疹疫情相关视频的情感分析标注数据集》，arXiv [cs.CY]，2024年，可访问：http://arxiv.org/abs/2406.07693 摘要：本数据集包含2024年1月1日至2024年5月31日期间互联网上264个网站发布的4011条关于当前麻疹疫情的视频数据。这些网站主要包括YouTube与TikTok，分别占视频总量的48.6%与15.2%；其余网站涵盖Instagram、Facebook，以及多家全球及本地新闻机构的官网。 数据集中为每条视频提供了独立属性字段，包括视频链接、帖子标题、帖子描述以及视频发布日期。构建完成本数据集后，研究团队针对视频标题与视频描述分别开展了情感分析（采用VADER工具）、主观性分析（采用TextBlob工具）以及细粒度情感分析（采用DistilRoBERTa-base模型）。具体分类任务包括：(i) 将每条视频标题与描述归入积极、消极或中性三类情感类别；(ii) 归入强主观、中性主观、弱主观三类主观性类别；(iii) 归入恐惧、惊讶、喜悦、悲伤、愤怒、厌恶或中性七类细粒度情感类别。 上述分析结果均以独立属性字段形式存储于数据集中，可用于该领域乃至其他应用场景下情感分析或主观性分析相关机器学习算法的训练与测试。本数据集配套论文（详见上述引用信息）同时列出了可基于本数据集开展探索的开放性研究问题列表。