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

Authors

Ralph Vincent J. Regalado, De La Salle University, Manila
Jenina L. Chua, De La Salle University, Manila
Justin L. Co, De La Salle University, Manila
Thomas James Z. Tiam-Lee, De La Salle University, Manila

College

College of Computer Studies

Document Type

Archival Material/Manuscript

Publication Date

2013

Abstract

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.

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Recommended Citation

Regalado, R. J., Chua, J. L., Co, J. L., & Tiam-Lee, T. Z. (2013). Subjectivity classification of Filipino text withfeatures based on term frequency - Inverse document frequency. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/13019

Disciplines

Computer Sciences

Keywords

Subjectivity (Linguistics); Computational linguistics; Filipino language—Semantics; Machine learning

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