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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Taboy, Clarisse"],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Master of Arts","degree_level":"Master","degree_discipline":"Linguistics","degree_department":null,"school":null,"contributors":[],"advisors":["Rivka Levitan"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02-01T08:00:00Z","date_published":"2023-02-01T08:00:00Z","updated_at":"2026-07-24T01:59:14Z","subjects":["Anthropological Linguistics and Sociolinguistics","Computational Linguistics","twitter","social media","sentiment analysis","filipino","filipinx","text classification"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/5234","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rivka Levitan"]},{"key":"dc:creator","label":"Author","values":["Taboy, Clarisse"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-02-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Linguistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Arts"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Anthropological Linguistics and Sociolinguistics","Computational Linguistics","twitter","social media","sentiment analysis","filipino","filipinx","text classification"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/5234"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["A Sentiment Analysis of \"Filipinx\" on Twitter Using a Multinomial Naïve Bayes Classification Model"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rivka Levitan"],"dc:creator":["Taboy, Clarisse"],"dc:date.available":["2023-02-01T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/5234"],"dc:subject":["Anthropological Linguistics and Sociolinguistics","Computational Linguistics","twitter","social media","sentiment analysis","filipino","filipinx","text classification"],"dc:title":["A Sentiment Analysis of \"Filipinx\" on Twitter Using a Multinomial Naïve Bayes Classification Model"],"thesis:degree_discipline":["Linguistics"],"thesis:degree_level":["Master"],"thesis:degree_name":["Master of Arts"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:59:14Z"}