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Full-Text Articles in Computational Linguistics
A Sentiment Analysis Of "Filipinx" On Twitter Using A Multinomial Naïve Bayes Classification Model, Clarisse Taboy
A Sentiment Analysis Of "Filipinx" On Twitter Using A Multinomial Naïve Bayes Classification Model, Clarisse Taboy
Dissertations, Theses, and Capstone Projects
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 …
An Examination Of Cross-Domain Authorship Attribution Techniques, Maxwell B. Schwartz
An Examination Of Cross-Domain Authorship Attribution Techniques, Maxwell B. Schwartz
Dissertations, Theses, and Capstone Projects
In recent years, Twitter has become a popular testing ground for techniques in authorship attribution. This is due to both the ease of building large corpora as well as the challenges associated with the character limit imposed by the service and the writing styles that have developed as a result. As both false and genuine claims of hacked Twitter accounts have made international news, there is an increasing need for this type of work. For newer Twitter accounts, however, there is little training data. Thus, this study looks to lay the groundwork for cross-domain authorship attribution: training on one source …