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Klassificering av svenska nyhetsartiklar med hjälp av Support Vector Machines
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2018 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Uppsatsen syftar till att minska omfattningen av påverkanskampanjer genom maskininlärningsmodellen Support Vector Machine. Arbetet utgår från en litteraturstudie samt två experiment. Litteraturstudien syftar till att ge en referensram till textklassificering med Support Vector Machines. Det första experimentet innebar träning av en Support Vector Machine för att klassificera svenska nyhetsartiklar utefter pålitlighet. Det andra experimentet innefattade en jämförelse av tränad SVM-modell och andra standardmetoder inom textklassificering. Resultaten från experimenten tyder på att SVM är ett effektivt verktyg för klassificering av svenska nyhetsartiklar men även att det finns fler modeller som är lämpliga för samma uppgift.

Abstract [en]

The aim of this paper is to reduce the extent of impact campaigns through use of the machine learning algorithm Support Vector Machine. The process involved a literature study and two experiments. The aim of the literature study was to give a frame of reference to text classification with Support Vector Machines. The first experiment involved training a SVM to be able to classify news articles written in swedish based on the reliability of the article. The second experiment involved a comparison between the trained SVM-model and other standard methods in the field. The results from the experiment indicates that SVM is a effective tool for classification of news articles written in Swedish, but also that other standard methods are suitable for the same task.

Place, publisher, year, edition, pages
2018. , p. 40
Keywords [sv]
SVM, support vector machines, nyheter, opålitliga nyheter, maskininlärning, machine learning, WEKA
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:hh:diva-37767OAI: oai:DiVA.org:hh-37767DiVA, id: diva2:1241232
Subject / course
Digital Forensics
Educational program
IT Forensics and Information Security, 180 credits
Supervisors
Examiners
Available from: 2018-10-15 Created: 2018-08-23 Last updated: 2018-10-15Bibliographically approved

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Blomberg, JossefinJansson Martén, Felicia
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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