Sentiment Analysis of YouTube Comments Using Bidirectional Encoder Representations from Transformers Neural Network Model

Pramila Gadyanavar, Mahantesh Laddi, Prashant Jadhav, Vaishali S Katti, Jambukeshwar Pujari, Azeem Javed Jamadar

2025

Abstract

Everything in the today’s world based on Sentiment. Sentiments are the Feelings that is based on Socially, Mentally, Economically, Psychologically Factors of the audience. Suppose you are multinational brand, and you want to know more about your Consumers Sentiments by figuring out by Looking at the people comment in your video at YouTube. It’s very hard to analyse comments line by line, word by word. Practically it’s not possible at that stage, because dealing with n numbers of comments are not possible. To Overcome these technical Situation, we are Introducing Our Sentiment Model that can Filtered the Audience Comments or sentiments. Sentiment Analysis is a natural Languages Processing Technique that is use know about the Sentiments in the text Mainly Positive Negative and Our model will Classify the YouTube comments Outcomes into five different Labels 1: "Very Negative", 2: "Negative" ,3: "Neutral" ,4: "Positive", 5: "Very Positive”. It will also give some Insights from the data using some Visualization Technique like Bar Graph, Pie chart and Word Cloud.

Paper Citation

in Harvard Style

Gadyanavar P., Laddi M., Jadhav P., Katti V., Pujari J. and Jamadar A. (2025). Sentiment Analysis of YouTube Comments Using Bidirectional Encoder Representations from Transformers Neural Network Model. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 683-689. DOI: 10.5220/0013640000004664

in Bibtex Style

@conference{incoft25,
author={Pramila Gadyanavar and Mahantesh Laddi and Prashant Jadhav and Vaishali S Katti and Jambukeshwar Pujari and Azeem Javed Jamadar},
title={Sentiment Analysis of YouTube Comments Using Bidirectional Encoder Representations from Transformers Neural Network Model},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={683-689},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013640000004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Sentiment Analysis of YouTube Comments Using Bidirectional Encoder Representations from Transformers Neural Network Model
SN - 978-989-758-763-4
AU - Gadyanavar P.
AU - Laddi M.
AU - Jadhav P.
AU - Katti V.
AU - Pujari J.
AU - Jamadar A.
PY - 2025
SP - 683
EP - 689
DO - 10.5220/0013640000004664
PB - SciTePress