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: firstname.lastname@example.org, email@example.com).
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).