A Sentiment Analysis of "Filipinx" on Twitter Using a Multinomial Naïve Bayes Classification Model

Author

Clarisse Taboy, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2023

Document Type

Thesis

Degree Name

M.A.

Program

Linguistics

Advisor

Rivka Levitan

Subject Categories

Anthropological Linguistics and Sociolinguistics | Computational Linguistics

Keywords

twitter, social media, sentiment analysis, filipino, filipinx, text classification

Abstract

On social media, the use of “Filipinx” as a gender neutral, inclusive term for “Filipino” tends to generate high user engagement, at times without regard for the original context in which the word appears. This project applies computational methods to collect a large dataset in English/Filipino from Twitter containing “Filipinx”, and to train a Naïve Bayes model to classify tweets into three sentiments: positive, neutral, and negative. My methodology takes inspiration from that of four related studies that similarly conducted sentiment analysis on English/Filipino tweets involving various topics, and whose resulting accuracy scores were compared side-by-side. Conducting sentiment analysis on tweets that mention “Filipinx” would meet four goals: to compare the model’s performance with those from the previous four studies, to create a larger-scale picture of user sentiments about the use of “Filipinx” than what I previously presented in a small-scale sociolinguistics project, and to contribute to conversations on how Filipino social media users discursively define Filipino identity.

Recommended Citation

Taboy, Clarisse, "A Sentiment Analysis of "Filipinx" on Twitter Using a Multinomial Naïve Bayes Classification Model" (2023). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5234