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Using machine learning to classify news articles
Linnaeus University, Faculty of Technology, Department of Computer Science.
Linnaeus University, Faculty of Technology, Department of Computer Science.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In today’s society a large portion of the worlds population get their news on electronicdevices. This opens up the possibility to enhance their reading experience bypersonalizing news for the readers based on their previous preferences. We have conductedan experiment to find out how accurately a Naïve Bayes classifier can selectarticles that a user might find interesting. Our experiments was done on two userswho read and classified 200 articles as interesting or not interesting. Those articleswere divided into four datasets with the sizes 50, 100, 150 and 200. We used a NaïveBayes classifier with 16 different settings configurations to classify the articles intotwo categories. From these experiments we could find several settings configurationsthat showed good results. One settings configuration was chosen as a good generalsetting for this kind of problem. We found that for datasets with a size larger than 50there were no significant increase in classification confidence.

Place, publisher, year, edition, pages
2016. , p. 26
Keywords [en]
Machine learning, Naive Bayes, News articles, text classification, WEKA
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:lnu:diva-59449OAI: oai:DiVA.org:lnu-59449DiVA, id: diva2:1058899
Supervisors
Examiners
Available from: 2016-12-22 Created: 2016-12-21 Last updated: 2016-12-22Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf