Youtube Comment Analyzer

Mohammed Arsalan Khan¹ , Sumit Baraskar² , Anshul Garg³ , Shineyu Khanna⁴ , Asha M.Pawar⁵

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.4 , pp.29-31, Aug-2021

Online published on Aug 31, 2021

Copyright © Mohammed Arsalan Khan, Sumit Baraskar, Anshul Garg, Shineyu Khanna, Asha M.Pawar . 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.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
The goal of this study article is to assist a content creator or anyone who wants to know about the audience`s thoughts, emotions, on a particular video. We have studied the literature papers first and then identified the basic functionality of it and then we get to know about its dimensions from the paper.

Key-Words / Index Term :
Sentiment Analysis; YouTube comment

References :
[1] Shi Yuan, Junjie Wu, Lihong Wang and Qing Wang, "A Hybrid Method for Multi-class Sentiment Analysis of Micro-blogs", ISBN- 978-1-5090-2842-9, 2016.
[2] Neethu M S and Rajasree R, “Sentiment Analysis in Youtube using Machine Learning Techniques”
[3] 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.
[4] 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.
[5] “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.
[6]Hearst, M., “Direction-based text interpretation as an information access refinement”, In Paul Jacobs, editor, Text-Based Intelligent Systems. Lawrence Erlbaum Associates, 1992.
[7]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.
[8]Argamon-Engelson, S., Koppel, M., and Avneri, G., “Style-based text categorization: What newspaper am I reading? ”, In Proc. of the AAAI Workshop on Text Categorization, pages 1–4, 1998.
[9]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.
[11]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.
[12]Wiebe, J., Wilson, T., and Cardie, C., “Annotating expressions of opinions and emotions in language”. Language Resources and Evaluation, 2005.