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Automatic irony- and sarcasm detection in Social media
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis looks at different methods that have been used for irony and sarcasm detection and also includes the design and programming of a machine learning model that classifies text as sarcastic or non-sarcastic. This is done with supervised learning. Two different data set where used, one with Amazon reviews and one from Twitter. An accuracy of 87% was obtained on the Amazon data with the Support Vector Machine. For the Twitter data was an accuracy of 71% obtained with the Adaboost classifier was used. The thesis is done in collaboration with Gavagai AB, which is company working with Big-data text with expertise in semantic analysis and opinion mining.

Place, publisher, year, edition, pages
2015. , 45 p.
Series
UPTEC F, ISSN 1401-5757 ; 15045
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:uu:diva-262242OAI: oai:DiVA.org:uu-262242DiVA: diva2:852975
External cooperation
Gavagai AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2015-09-14 Created: 2015-09-10 Last updated: 2015-09-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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