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Sentiment Analysis of Nordic Languages
Halmstad University, School of Information Technology. Sweden.
Halmstad University, School of Information Technology.
2019 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis explores the possibility of applying sentiment analysis to extract tonality of user reviews on the Nordic languages. Data processing is performed in the form of preprocessing through tokenization and padding. A model is built in a framework called Keras. Models for classification and regression were built using LSTM and GRU architectures. The results showed how the dataset influences the end result and the correlation between observed and predicted values for classification and regression. The project shows that it is possible to implement NLP in the Nordic languages and how limitations in input and performance in hardware affected the result. Some questions that arose during the project consist of methods for improving the dataset and alternative solutions for managing information related to big data and GDPR.

Abstract [sv]

Denna avhandling undersöker möjligheten att tillämpa sentiment analys för att extrahera tonalitet av användarrecensioner på nordiska språk. Databehandling utförs i form av förprocessering genom tokenisering och padding. En modell är uppbyggd i en ramverkad Keras. Modeller för klassificering och regression byggdes med LSTM och GRU-arkitekturer. Resultaten visade hur datasetet påverkar slutresultatet och korrelationen mellan observerade och förutspådda värden för klassificering och regression. Projektet visar att det är möjligt att implementera NLP på de nordiska språken och hur begränsningar i input och prestanda i hårdvara påverkat resultatet. Några frågor som uppstod under projektet består av metoder för att förbättra datasetet och alternativa lösningar för hantering av information relaterad till stora data och GDPR.

Place, publisher, year, edition, pages
2019. , p. 47
Keywords [en]
Neural networks, LSTM, GRU, Keras, Sentiment analysis, Nordic languages
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-39884OAI: oai:DiVA.org:hh-39884DiVA, id: diva2:1327626
External cooperation
QuickSearch Sweden AB
Subject / course
Computer science and engineering; Computer science and engineering
Educational program
Computer Science and Engineering, 300 credits; Computer Engineer, 180 credits
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-19 Last updated: 2019-06-24Bibliographically approved

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CiteExportLink to record
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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