International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 8, Issue 2 (February 2021), Pages: 77-84

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 Original Research Paper

 Title: Content analytics based on random forest classification technique: An empirical evaluation using online news dataset

 Author(s): Puteri N. E. Nohuddin 1, *, Wan M. U. Noormanshah 1, Zuraini Zainol 2

 Affiliation(s):

 1Institute of IR4.0, National University of Malaysia, Bangi, Malaysia
 2Department of Computer Science, Faculty of Science and Defence Technology, National Defence University of Malaysia, Kuala Lumpur, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-0627-5630

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.02.011

 Abstract:

In this paper, a study is established for exploiting a document classification technique for categorizing a set of random online documents. The technique is aimed to assign one or more classes or categories to a document, making it easier to manage and sort. This paper describes an experiment on the proposed method for classifying documents effectively using the decision tree technique. The proposed research framework is a Document Analysis based on the Random Forest Algorithm (DARFA). The proposed framework consists of 5 components, which are (i) Document dataset, (ii) Data Preprocessing, (iii) Document Term Matrix, (iv) Random Forest classification, and (v) Visualization. The proposed classification method can analyze the content of document datasets and classifies documents according to the text content. The proposed framework use algorithms that include TF-IDF and Random Forest algorithm. The outcome of this study benefits as an enhancement to document management procedures like managing documents in daily business operations, consolidating inventory systems, organizing files in databases, and categorizing document folders. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Classification, Random forest, Document term matrix, Term frequency–inversed document, Frequency

 Article History: Received 21 June 2020, Received in revised form 1 October 2020, Accepted 7 October 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Nohuddin PNE, Noormanshah WMU, and Zainol Z (2021). Content analytics based on random forest classification technique: An empirical evaluation using online news dataset. International Journal of Advanced and Applied Sciences, 8(2): 77-84

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 Figures

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 Tables

 Table 1

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