YouTube Video Analyzer Using Sentiment Analysis

Authors

  • Goli Sushma Assistant Professor, Department of CSE (Data Science), Gurunanak institutions Technical Campus, Hyderabad
  • Vadicherla Raju Assistant Professor , Department of CSE(Data Science), Vignana Bharathi Institute of Technology, Hyderabad, India
  • Rajashekar Kandakatla Assistant Professor, Department of CSE ( Artificial Intelligence and Machine Learning), Vaagdevi College of Engineering(Autonomous), Bollikunta, Warangal, Telangana
  • Neethipudi Sashi Prabha Assistant Professor , Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Technology, Hyderabad
  • Rajesh Saturi Associate Professor Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, India
  • L. Mohan Associate Professor, Dept. CSE(Internet of Things), Balaji Institute of Technology and Science(Autonomous), Narsampeta, Warangal, Telangana

Keywords:

Sentiment analysis, Natural Language Processing, Opinion extraction, Decision–making

Abstract

Sentiment analysis is a method used to ascertain the opinions and viewpoints of people regarding any service or product. With millions of views, YouTube is one of the most widely used sites for sharing videos. With the ever-increasing popularity of online videos and the exponential growth of user-generated content, understanding the quality and relevancy of content has become crucial for viewers by looking over the comments, number of views, and number of likes manually. The goal of this paper is to create a thorough methodology and a useful tool for assessing user sentiment on YouTube videos. The suggested method extracts text comments and transcripts from YouTube videos for examination using cutting-edge natural language processing algorithms and categorizes them into opinions that are neutral, positive, negative, relevant, and irrelevant along with a transcript summary. Ultimately, this research endeavors to revolutionize the way YouTube videos are analyzed, facilitating informed decision-making and enhancing user experience on the platform.

Downloads

Download data is not yet available.

References

Mohammed Arsalan Khan, SumitBaraskar, Anshul Garg, Shineyu Khanna, Asha M. Pawar, “YouTube Comment Analyzer”, International Journal of Scientific Research in Computer Science and Engineering, August 2021

Aditya Baravkar, RishabhJaiswal, JayeshChhoriya, “Sentimental Analysis of YouTube Videos”, International Research Journal of Engineering and Technology (IRJET), Volume: 07, Issue: 12, Dec 2020

K.M. Kavitha, Asha Shetty, Bryan Abreo, Adline D’Souza, AkarshaKondana, “Analysis and Classification of User Comments on YouTube Videos”, ScienceDirect, Nov 2020

Deepali K., Gaikwad, C. NamrataMahender, “A Review Paper on Text Summarization”, International Journal of Advanced Research in Computer and Communication Engineering

DuyDucAn Bui PhD, Guilherne DelFiol MD, PhD, John F. Hurdle MD, PhD, Siddhartha Jonnalagadda PhD, “Extractive text summarization system to aid data extraction from full text in systematic review development”, Journal of Biomedical Informatics, Oct 2016.

Shi Yuan, Junjie Wu, Lihong Wang and Qing Wang, "A Hybrid Method for Multi-class Sentiment Analysis of Microblogs", ISBN- 978-1-5090-2842-9, 2016.

Neethu M S and Rajasree R, “Sentiment Analysis in Youtube using Machine Learning Techniques”

Aliza Sarlan, Chayanit Nadam, and Shuib Basri, "Youtube Sentiment Analysis", 2014 International Conference on Information Technology and Multimedia (ICIMU), Putrajaya, Malaysia November 18 – 20, 2014.

B. Gupta, M. Negi, K. Vishwakarma, G. Rawat, and P. Bandhani, "Study of Youtube Sentiment Analysis using Machine Learning Algorithms on Python," Int. J. Comput. Appl., vol. 165, no. 9, pp. 29–34, May 2017.

“Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing," 2019 IEEE Comput. Soc. Annu. Symp. On, p. 134, 2019. Opinion Mining”, Kluwer Academic Publishers. Printed in the Netherlands, 2006.

Hearst, M., “Direction-based text interpretation as an information access refinement”, In Paul Jacobs, editor, Text Based Intelligent Systems. Lawrence Erlbaum Associates, 1992.

Das, S., and Chen, M., “Yahoo! for Amazon: Extracting market sentiment from stock message boards”, In Proc. of the 8th Asia Pacific Finance Association Annual Conference (APFA 2001), 2001. unsupervised classification of reviews”. In Proc. of the ACL, 2002.

Argamon-Engelson, S., Koppel, M., and Avneri, G., “Stylebased text categorization: What newspaper am I reading? ”, In Proc. of the AAAI Workshop on Text Categorization, pages 1–4, 1998.

Pang, B. & Lee, L., “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts”, Association of Computational Linguistics (ACL), 2004. [10]Jin, W., & HO H. H., “A novel lexicalized HMM-based learning framework for web opinion mining”, Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec, Canada, ACM: 465-472, 2009.

Brody, S., & Elhadad, N., “An unsupervised aspect-sentiment model for online reviews”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Los Angeles, California, Association for Computational Linguistics: 804-812, 2010.

Wiebe, J., Wilson, T., and Cardie, C., “Annotating expressions of opinions and emotions in language”. Language Resources and Evaluation, 2005.

https://ieeexplore.ieee.org/document/9396049

https://www.sciencedirect.com/science/article/pii/S1877050920323553

https://www.isroset.org/pdf_paper_view.php?paper_id=2461&6-ISROSET-IJSRCSE- 06279.pdf

Downloads

Published

23.02.2024

How to Cite

Sushma, G. ., Raju, V. ., Kandakatla, R. ., Prabha, N. S. ., Saturi, R. ., & Mohan, L. . (2024). YouTube Video Analyzer Using Sentiment Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 597–601. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4897

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.