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Classifying Stock Market Tweets: Sentiment analysis applied to tweets published by Swedish stock market influencers
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Stock market enthusiasts have hooped around Twitter to exchange their opinions about companies and new investment cases. New qualitative information and questioning discussions are carried out on a daily basis. Non or less frequent Twitter users are at risk of missing out of good and relevant information. This thesis investigates if sentiment analysis can be used to classify tweets published by stock market influencers as positive or negative. Can this machine learning approach be used within an application to present relevant information? The two machine learning algorithms Naïve Bayes and Support Vector Machine are applied to historical tweets. Their performance of classifying historical tweets published by ten stock market influencers are evaluated. The results shows accuracies ranging from 68.6 - 75.6% for Naïve Bayes and 76.1 -78.9% for the Support Vector Machine.

Place, publisher, year, edition, pages
2017. , p. 23
Series
UMNAD ; 1125
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-142511OAI: oai:DiVA.org:umu-142511DiVA, id: diva2:1161779
Educational program
Bachelor of Science Programme in Computing Science
Supervisors
Examiners
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • 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
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  • asciidoc
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