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Sentiment analysis of Swedish social media: Using random indexing to improve cross-domain sentiment classification
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Social media has grown extremely fast in recent years andin the vast number of posts being made everyday people expresstheir opinions about all kinds of topics. These opinionsare very valuable and there is a need for a way toautomatically identify and extract them. This is what sentimentanalysis is about but there are a number of issuesrelated to this task. In particular the large number anddiversity of the texts to analyze causes problems for ordinarymethods of natural language processing. In this thesisa method utilizing a technique called Random Indexing isproposed which tries to overcome some of the issues. Theconclusion is that the use of Random Indexing does aid insolving the problem but also that more work is needed inorder to have a fully satisfying solution.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-156249OAI: oai:DiVA.org:kth-156249DiVA: diva2:766075
Examiners
Available from: 2014-11-26 Created: 2014-11-26 Last updated: 2014-11-26Bibliographically approved

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fulltext(585 kB)96 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • 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