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IJMLC 2020 Vol.10(2): 406-411 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.950

Analyzing Hot Facebook Users Posts’ Sentiment Using Deep Learning

Nguyen Ngoc Tram and Phan Duy Hung

Abstract—It’s almost the end of 2019, the second decade of the 21st century, and the world has changed a lot. With the development of Internet and social networks, people have got all kinds of new entertainment and also new careers. Money now can be earned by: sitting in one place and streaming games; reviewing food, movies, music; or just simply showing a pretty face. Social networks make all of those things, and more, possible. As long as a person is famous on social networks, they can earn money by doing almost everything. So how to be famous, or in social network’s language, what gets someone “followers” on social media? Is being positive, telling funny stories, showing sunshine and flower pictures enough? Or they can be a pessimistic person, ranting about everything and people still worship them like a god? This research collects and analyzes hot Facebook users posts’ sentiment to see if what someone posts on Facebook could make them famous, and also determines the accuracy of using Deep Learning in analyzing Vietnamese social media contents sentiment. There have been several studies for social media content sentiment analyzing, but none with Vietnamese social media contents of famous Vietnamese people. In this study, a data set of Vietnamese hot Facebook users’ posts is labeled and organized to be shared with the language research community generally, and the Vietnamese language research specifically.

Index Terms—Content sentiment analyzing, long short-term memory, social media, recurrent neural network, Vietnamese hot Facebook users’ posts.

Nguyen Ngoc Tram and Phan Duy Hung are with FPT University, Hanoi, Vietnam (e-mail: tramnnmse0088@ftp.edu.vn, hungpd2@fe.edu.vn).

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Cite: Nguyen Ngoc Tram and Phan Duy Hung, "Analyzing Hot Facebook Users Posts’ Sentiment Using Deep Learning," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 406-411, 2020.

Copyright © 2020 by the authors. 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 (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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