Subjectivity classification of Filipino text with features based on term frequency - inverse document frequency
Added Title
International Conference on Asian Language Processing (2013)
IALP 2013
College
College of Computer Studies
Department/Unit
Software Technology
Document Type
Conference Proceeding
Source Title
Proceedings - 2013 International Conference on Asian Language Processing, IALP 2013
First Page
113
Last Page
116
Publication Date
12-1-2013
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. © 2013 IEEE.
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Digitial Object Identifier (DOI)
10.1109/IALP.2013.40
Recommended Citation
Regalado, R. J., Chua, J. L., Co, J. L., & Tiam-Lee, T. Z. (2013). Subjectivity classification of Filipino text with features based on term frequency - inverse document frequency. Proceedings - 2013 International Conference on Asian Language Processing, IALP 2013, 113-116. https://doi.org/10.1109/IALP.2013.40
Disciplines
Computer Sciences
Keywords
Machine learning; Subjectivity (Linguistics); Filipino language--Semantics--Data processing
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