Subjectivity classification of Filipino text withfeatures based on term frequency - Inverse document frequency

• Main Authors: Regalado, Ralph Vincent J., Chua, Jenina L., Co, Justin L., Tiam-Lee, Thomas James Z.
• Format: text
• Published: Animo Repository 2013
• Subjects: Subjectivity (Linguistics); Computational linguistics; Filipino language—Semantics; Machine learning; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/13019
• Institution: De La Salle University

Subjectivity classification classifies a given document if it contains subjective information or not, or identifies which portions of the document are subjective. This research reports a machine learning approach on document-level and sentence-level subjectivity classification of Filipino texts using existing machine learning algorithms such as C4.5, Naïve Bayes, k-Nearest Neighbor, and Support Vector Machine. For the document-level classification, result shows that Support Vector Machines gave the best result with 95.06% accuracy. While for the sentence-level classification, Naïve Baves gave the best result with 58.75% accuracy.