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Analysing Social Media Marketing on Twitter using Sentiment Analysis
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Attitydanalys av marknadsföring på Twitter (Swedish)
Abstract [en]

Social media is an increasingly important marketing platform in today’s society, and many businesses use them in one way or another in their advertising. This report aimed to determine the effect of different factors on the sentiment in the response to a tweet posted on Twitter for advertising purposes by companies in the fast food sector in North America. The factors considered were the time of posting, the length and the sentiment of a tweet, along with the presence of media other than text in the tweet. Sentiment was extracted from samples of the response to the advertising tweets collected daily between the 27th of March and the 28th of April and plotted against the factors mentioned. The results indicate that the sentiment of the advertising tweet along with the time of posting had the biggest impact on the response, though no definitive conclusions on their effects could be drawn.

Abstract [sv]

Sociala medier är en allt viktigare marknadsföringsplattform i dagens samhälle, och många företag använder dem på ett eller annat sätt i sin marknadsföring. Syftet med denna studie är att genom attitydanalys undersöka hur ett antal faktorer inom marknadsföring på det sociala mediet Twitter påverkar responsen till den. Dessa faktorer var följande: inläggets tid, längd och attityd, samt förekomst av media i inlägget. Inläggen samlades från Twitter mellan 28. mars och 28. april och attityden i dem mättes genom attitydanalys, varpå attityden i svaren till reklaminläggen jämfördes baserat på de ovannämnda faktorerna. Resultaten visar på att attityden i reklaminläggen och tiden då de läggs upp har störst påverkan på hur svaren ser ut, men inga säkra slutsatser har kunnat dras.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:199
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-229787OAI: oai:DiVA.org:kth-229787DiVA, id: diva2:1214465
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Examiners
Available from: 2018-06-26 Created: 2018-06-06 Last updated: 2018-06-26Bibliographically approved

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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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