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: https://doi.org/10.48550/arXiv.2406.07693AbstractThis 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 及 A. Bian，《针对YouTube、TikTok及其他平台上2024年麻疹暴发相关视频的情感分析标注数据集》，arXiv [cs.CY]，2024年。获取链接：https://doi.org/10.48550/arXiv.2406.07693。 本数据集涵盖2024年1月1日至2024年5月31日期间，互联网上264个网站发布的共计4011条与2024年麻疹暴发相关的视频数据。其中，YouTube和TikTok平台的视频占比分别为48.6%与15.2%，为占比最高的两大发布渠道；其余发布网站包括Instagram、Facebook，以及多家全球及本地新闻机构的官方网站。 数据集为每条视频设置了独立属性字段，包含视频链接、帖子标题、帖子描述及视频发布日期。 数据集构建完成后，研究人员针对视频标题与视频描述开展了三类分析：其一为情感分析（sentiment analysis），采用VADER工具；其二为主观性分析（subjectivity analysis），采用TextBlob工具；其三为细粒度情感分析（fine-grain sentiment analysis），采用DistilRoBERTa-base模型。分析内容包括将每条视频的标题与描述归类为：（i）情感类别（正面、负面或中性）；（ii）主观性类别（强主观、中性主观或弱主观）；（iii）细粒度情感类别（恐惧、惊讶、喜悦、悲伤、愤怒、厌恶或中性）。 上述分析结果均作为独立属性字段纳入数据集，可用于该领域乃至其他领域情感分析、主观性分析相关机器学习算法的训练与测试，亦可应用于其他相关场景。本数据集配套论文（即前述引用文献）还列出了可基于本数据集开展研究的开放式科研问题清单。