Version 1
: Received: 24 April 2024 / Approved: 25 April 2024 / Online: 26 April 2024 (08:15:37 CEST)
How to cite:
Tsiourlini, M.; Tzafilkou, K.; Karapiperis, D.; Tjortjis, C. Text Analytics on YouTube Comments for Food Products. Preprints2024, 2024041691. https://doi.org/10.20944/preprints202404.1691.v1
Tsiourlini, M.; Tzafilkou, K.; Karapiperis, D.; Tjortjis, C. Text Analytics on YouTube Comments for Food Products. Preprints 2024, 2024041691. https://doi.org/10.20944/preprints202404.1691.v1
Tsiourlini, M.; Tzafilkou, K.; Karapiperis, D.; Tjortjis, C. Text Analytics on YouTube Comments for Food Products. Preprints2024, 2024041691. https://doi.org/10.20944/preprints202404.1691.v1
APA Style
Tsiourlini, M., Tzafilkou, K., Karapiperis, D., & Tjortjis, C. (2024). Text Analytics on YouTube Comments for Food Products. Preprints. https://doi.org/10.20944/preprints202404.1691.v1
Chicago/Turabian Style
Tsiourlini, M., Dimitrios Karapiperis and Christos Tjortjis. 2024 "Text Analytics on YouTube Comments for Food Products" Preprints. https://doi.org/10.20944/preprints202404.1691.v1
Abstract
YouTube is a very popular social media platform in today’s digital landscape. The primary
focus of this study is to explore the underlying sentiment in user comments on food-related videos
on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic.
Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER.
Furthermore, the sentiments of the comments were classified using advanced Machine Learning
(ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest,
Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average
metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high
accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic
dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets,
measuring crucial metrics such as views, likes, comments, and engagement rate. The findings
illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but
the plant-based dataset maintains a superior overall engagement rate.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
