Classifying Indonesian online articles as advertisement placement base using text mining

Nadhira Tasya, Arian Dhini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Rapid development in technological aspect resulting in growing level of human needs for the latest news, so that emerged a new trend of publishing and accessing news through online media or usually called online journalism. In addition, the number of people who sell and purchase through online sites also continues to increase and this opportunity is utilized by the company and the advertiser by implementing targeted web advertising. However, the high number of articles that have been published and accessed leads to great opportunities for errors in determining where to place the ads. Therefore, it needs a system that can categorize articles accessed by users as the basis of advertisement placement by the company and this classification system can be done by applying the method of Data Mining and Text Mining. This research uses document data in the form of article content that will be categorized into twenty categories of class of advertisement by using Text Mining technique with Support Vector Machine algorithm. The results of this study may be used by companies or advertisers as a basis for placement of ads on selected online media sites.

Original languageEnglish
Title of host publication2017 International Conference on Business and Information Management, ICBIM 2017
PublisherAssociation for Computing Machinery
Pages107-111
Number of pages5
ISBN (Electronic)9781450352765
DOIs
Publication statusPublished - 23 Jul 2017
Event2017 International Conference on Business and Information Management, ICBIM 2017 - Beijing, China
Duration: 23 Jul 201725 Jul 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F131932

Conference

Conference2017 International Conference on Business and Information Management, ICBIM 2017
CountryChina
CityBeijing
Period23/07/1725/07/17

Keywords

  • Multi Label Classification
  • Online articles
  • Support Vector Machine
  • Targeted web advertising
  • Text Mining

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