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Sentiment analysis and transfer learning using recurrent neural networks: an investigation of the power of transfer learning
Linköping University, Department of Computer and Information Science, Human-Centered systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Sentimentanalys och överföringslärande med neuronnät (Swedish)
Abstract [en]

In the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a Swedish price comparison site, and hotels.com this work investigate how the similarities between domains affect the results of transfer learning when using recurrent neural networks. We test several different domains with different characteristics, e.g. size and lexical similarity. In this work only relatively similar domains were used, the same target function was sought and all reviews were in Swedish. Regardless, the results are conclusive; transfer learning is often beneficial, but is highly dependent on the features of the domains and how they compare with each other’s.

Place, publisher, year, edition, pages
2019. , p. 40
Keywords [en]
Machine Learning, Neural Networks, Transfer Learning, Domain Adaption, Sentiment Analysis
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161348ISRN: LIU-IDA/LITH-EX-A--19/080—SEOAI: oai:DiVA.org:liu-161348DiVA, id: diva2:1366704
External cooperation
Findwise AB
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved

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fulltext(1060 kB)16 downloads
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CiteExportLink to record
Permanent link

Direct link
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