Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments
Ronel J. Bilog

Ronel J. Bilog*, Junior Lecturer, Department of Computer Education, City College of Calamba, Calamba City, Philippines.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 03, 2020. | Manuscript published on January 15, 2020. | PP: 73-77 | Volume-4 Issue-5, January 2020 | Retrieval Number: E0524014520/2020©BEIESP | DOI: 10.35940/ijmh.E0524.014520
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Abstract: The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.
Keywords: English, Filipino, Naïve Bayes Algorithm, Sentiment Analysis, Social Media, Taglish.