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

Citation Author(s):
Nirmalya
Thakur
Department of Computer Science, Emory University
Vanessa
Su
Department of Mathematics, Emory University
Mingchen
Shao
Department of Computer Science, Emory University
Kesha A.
Patel
Department of Mathematics, Emory University
Hongseok
Jeong
Department of Computer Science, Emory University
Victoria
Knieling
Program in Linguistics, Emory University
Andrew
Bian
Goizueta Business School, Emory University
Submitted by:
Nirmalya Thakur
Last updated:
Wed, 06/19/2024 - 15:54
DOI:
10.21227/40s8-xf63
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Abstract 

To access this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/doi/10.5281/zenodo.11711229

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.2406.07693

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.

 

Instructions: 

The instructions for the usage of this dataset are presented in the associated paper. Please see the above-mentioned citation for the paper. 

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