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10.1109/IALP.2013.40guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Subjectivity Classification of Filipino Text with Features Based on Term Frequency -- Inverse Document Frequency

Published: 17 August 2013 Publication History

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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  • (2017)Emotion Classification of Duterte Administration Tweets Using Hybrid ApproachProceedings of the 2017 International Conference on Software and e-Business10.1145/3178212.3178233(22-27)Online publication date: 28-Dec-2017
  1. Subjectivity Classification of Filipino Text with Features Based on Term Frequency -- Inverse Document Frequency

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    cover image Guide Proceedings
    IALP '13: Proceedings of the 2013 International Conference on Asian Language Processing
    August 2013
    261 pages
    ISBN:9780769550633

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    IEEE Computer Society

    United States

    Publication History

    Published: 17 August 2013

    Author Tags

    1. Filipino language
    2. TF-IDF
    3. machine learning approach
    4. subjectivity classification

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    • (2017)Emotion Classification of Duterte Administration Tweets Using Hybrid ApproachProceedings of the 2017 International Conference on Software and e-Business10.1145/3178212.3178233(22-27)Online publication date: 28-Dec-2017

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