UNDERSTANDING AUDIENCE SENTIMENT: A COMPREHENSIVE SENTIMENT ANALYSIS SYSTEM FOR YOUTUBE COMMENTS

Registration ID: IJNRD_217984

Published ID: IJNRD2404353

DOI: Click Here to Get

Pandhare Nilesh , Akshay Londhe , Lokesh Wani , Vaibhav Waghmare , Prof. M. S. Bhosale

Sentiment analysis, YouTube comments, Random Forest, Machine learning, Data visualization

This paper introduces a sentiment analysis system tailored for YouTube comments, leveraging the Random Forest algorithm for sentiment classification. The system automates the categorization of comments into positive, negative, or neutral sentiments, offering valuable insights into audience sentiment towards video content. The study encompasses data collection, preprocessing, model training, evaluation, and result visualization. Results demonstrate the efficacy of Random Forest in accurately categorizing comments and visualizing sentiment distribution. Non-standard abbreviations are avoided, and the abstract stands independently without references or citations. The paper encourages further exploration of Random Forest in sentiment analysis for online platforms like YouTube

Published Paper   E-Certificate

Pandhare Nilesh, Akshay Londhe, Lokesh Wani, Vaibhav Waghmare, & Prof. M. S. Bhosale (April-2024). UNDERSTANDING AUDIENCE SENTIMENT: A COMPREHENSIVE SENTIMENT ANALYSIS SYSTEM FOR YOUTUBE COMMENTS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), d464-d471. https://ijnrd.org/papers/IJNRD2404353.pdf

Volume 9 Issue 4, April-2024

Pages : d464-d471

Paper Reg. ID: IJNRD_217984

Published Paper Id: IJNRD2404353

Downloads: 000121985

Research Area: Information Technology 

Country: Pune, maharastra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404353.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404353

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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