ACC

Article http://dx.doi.org/10.26855/acc.2025.01.006

Sentiment Analysis on YouTube Data: A Comparison of TextBlob and VADER

TOTAL VIEWS: 476

Haowen Zhang

University of California, San Diego, San Diego, CA 92092, USA.

*Corresponding author: Haowen Zhang

Published: March 4,2025

Abstract

This article tried to compare the performance of TextBlob and VADER on analyzing comments under YouTube videos and measured the correlation between using sentimental analyzers to predict the quality of videos and using the like/views ratio to compare the quality of videos. People are becoming busy, and they rely more on watching videos to have fun, acquire new knowledge, and exchange thoughts. Finding a way to estimate the quality of the videos becomes important. 18000 comments are collected from 60 videos from 6 categories. TextBlob and VADER are used to analyze these comments. Scores are given by these two sentimental analyzers. The scores of each video are compared to the likes/views ratio to predict the quality of the videos. Charts are made to analyze the trends. The result is that TextBlob is more suitable for this model because the scores it gives are more stable and have a positive correlation to the likes/views model.

References

[1] Devakunchari R. Analysis on big data over the years. Int J Sci Res Publ. 2014 Jan;4(1).

[2] Kumar A, Sebastian TM. Sentiment Analysis: A Perspective on its Past, Present and Future. Int J Intell Syst Appl. 2012;4(10):1-14. https://doi.org/10.5815/ijisa.2012.10.01

[3] Aljedaani W. Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowl-Based Syst. 2022 Nov 14;255:109780. https://www.sciencedirect.com/science/article/pii/S0950705122009017.

[4] Rochadiani TH. Sentiment Analysis of YouTube comments toward Chat GPT. J Transformatika. 2023;21(1).

https://doi.org/10.26623/transformatika.v21i2.7033.

[5] Yue L, Chen W, Li X, Zuo W, Yin M. A survey of sentiment analysis in social media. Knowl Inf Syst. 2018;60(2):617-663.

https://doi.org/10.1007/s10115-018-1236-4.

[6] Jones T, Cuthrell K. YouTube: Educational Potentials and Pitfalls. Comput Sch. 2011;28(1):75-85.

https://doi.org/10.1080/07380569.2011.553149.

[7] Berger I. YouTube as a source of data. PsyPAG Q. 2012 Jun;(83):9.
https://d1wqtxts1xzle7.cloudfront.net/30868316/PsyPAG-83-June-2012-libre.pdf?1392134977=&response-content-disposition=inline%3B+filename%3DYoutube_as_a_source_of_data.pdf&

Ex-pires=1731808685&Signature=bPJFa7SAMZrtdGABzgyDv5J6eSlqrJ3q4fH6CpFdZnN30GGktyLAX-Mi4cubRk-KB8eQJxY

93d64UnT~xZ9PK~qfSacsj4hjtggc6SwMQZfmMhzvzP85eFPku2CedKCr-Jv5cyz2FNG7~9MqR8a-A6Kr7ElQMFjOF~x0tziLv

vjC~L1cCjyifrwJzqa3ztQ-mA0~C-myuMNeFJLL1~o-OhzJbwvZWV7ygmMlT3~ExDghcFPPm0Xnu~IZbwjAuOxQ5OAIXiM

XrgbmOrhDwcDlbG5O6c22KAHSrvtAzTYTzqH85bSuSFB-wZc25uZWWMHQBQqkcK58L-rVQvgIyZk8xg__&Key-Pair-Id

=APKAJLOHF5GGSLRBV4ZA#page=11.

[8] Umer M. Sentiment analysis of tweets using a unified convolutional neural network-long short-term memory network model. Comput Intell. 2021 Feb;37(1):409-434. https://onlinelibrary.wiley.com/doi/full/10.1111/coin.12415.

[9] Nguyen TH, Shirai K, Velcin J. Sentiment analysis on social media for stock movement prediction. Expert Syst Appl. 2015;42(24):9603-9611.
https://doi.org/10.1016/j.eswa.2015.07.052.

[10] Chandra RV, Varanasi BS. Python requests essentials. Packt Publishing Ltd; 2015.

[11] textblob.readthedocs.io [Internet]. Available from: https://textblob.readthedocs.io/en/dev/.

[12] Aljedaani W, Rustam F, Mkaouer MW, Ghallab A, Rupapara V, Washington PB, et al. Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowl-Based Syst. 2022;255:109780.

https://doi.org/10.1016/j.knosys.2022.109780.

[13] Marsanich G. Comparing Predictions of YouTube Video Like to Dislike Ratios Using Sentiment Analysis Tools [thesis]. Tilburg University; [cited 2023]. Available from: http://arno.uvt.nl/show.cgi?fid=161975.

[14] Hutto CJ, Gilbert E. VADER: A Parsimonious Rule-based Model for. In: Eighth Int AAAI Conf Weblogs Soc Media. 2014. p. 18. Available from:
https://ojs.aaai.org/index.php/icwsm/article/view/14550.

[15] Rousidis D, Koukaras P, Tjortjis C. Social media prediction: a literature review. Multimed Tools Appl. 2020;79(9-10).

[16] Abiola O, et al. Sentiment Analysis of Covid-19 Tweets from Selected Hashtags in Nigeria Using Vader and Text Blob Analyser. J Electr Syst Inf Technol. 2023 Jan 16. https://doi.org/10.1186/s43067-023-00070-9.

[17] Devi BL, Bai VV, Ramasubbareddy S, Govinda K. Sentiment analysis on movie reviews. In: Advances in Intelligent Systems and Computing. 2020. p. 321-328. https://doi.org/10.1007/978-981-15-0135-7_31.

How to cite this paper

Sentiment Analysis on YouTube Data: A Comparison of TextBlob and VADER

How to cite this paper: Haowen Zhang. (2025) Sentiment Analysis on YouTube Data: A Comparison of TextBlob and VADER. Advances in Computer and Communication6(1), 35-40.

DOI: http://dx.doi.org/10.26855/acc.2025.01.006