关于2024年麻疹暴发的YouTube、TikTok及其他来源视频情感分析标注数据集
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles
AI总结：
该论文构建了一个包含4011个关于2024年麻疹暴发视频的标注数据集，利用VADER、TextBlob和DistilRoBERTa-base进行情感、主观性和细粒度情感分析，并提出了开放研究问题。
摘要：
本文工作呈现了一个数据集，该数据集包含2024年1月1日至2024年5月31日期间在互联网上264个网站发布的4011个关于当前麻疹暴发的视频数据。该数据集可在https://dx.doi.org/10.21227/40s8-xf63获取。这些网站主要包括YouTube和TikTok，分别占视频总数的48.6%和15.2%。其余网站包括Instagram和Facebook，以及各种全球和地方新闻机构的网站。对于每个视频，数据集中以单独属性呈现视频的URL、帖子标题、帖子描述和视频发布日期。在开发此数据集后，对视频标题和视频描述进行了情感分析（使用VADER）、主观性分析（使用TextBlob）和细粒度情感分析（使用DistilRoBERTa-base）。这包括将每个视频标题和视频描述分类为（i）情感类别之一，即积极、消极或中性；（ii）主观性类别之一，即高度观点性、中性观点性或低观点性；以及（iii）细粒度情感类别之一，即恐惧、惊讶、喜悦、悲伤、愤怒、厌恶或中性。这些结果作为数据集中的单独属性呈现，用于训练和测试在该领域执行情感分析或主观性分析的机器学习算法以及其他应用。最后，本文还提出了一系列可利用该数据集进行研究的开放研究问题。
英文摘要：
The work of this paper presents a dataset that 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. The dataset is available at https://dx.doi.org/10.21227/40s8-xf63. 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. Finally, this paper also presents a list of open research questions that may be investigated using this dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
