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undergraduate thesis
Postupci analize sentimenta kroz domene za hrvatski jezik
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:457933
Kašljević, Vinko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Kašljević, V. (2018). Postupci analize sentimenta kroz domene za hrvatski jezik (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:457933

Kašljević, Vinko. "Postupci analize sentimenta kroz domene za hrvatski jezik." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:457933

Kašljević, Vinko. "Postupci analize sentimenta kroz domene za hrvatski jezik." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:457933

Kašljević, V. (2018). 'Postupci analize sentimenta kroz domene za hrvatski jezik', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:457933

Kašljević V. Postupci analize sentimenta kroz domene za hrvatski jezik [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 August 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:457933

V. Kašljević, "Postupci analize sentimenta kroz domene za hrvatski jezik", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:457933

Metadata
TitlePostupci analize sentimenta kroz domene za hrvatski jezik
Title (english)Cross-Domain Sentiment Analysis Methods for Croatian Language
AuthorVinko Kašljević
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberMladen Karan (član povjerenstva)
Committee memberDomagoj Alagić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-09-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Danas postoji trend sve većeg korištenja strojnog učenja za različite probleme. Jedan od tih problema je obrada prirodnog jezika gdje strojno učenje postiže jako dobre rezultate. Da bi mogli koristiti strojno učenje potrebni su nam podaci te iako podataka imamo više nego ikada prije, nama su potrebi dobro označeni podaci. Tu nam može pomoći prilagodba domene. U ovom radu objašnjavamo što je strojno učenje, što je prilagodba domene te kako nam ona može pomoći pri analizi sentimenta za različite domene. Zatim razmatramo praktični dio ovog rada: priprema podataka, odabir modela te izvođenje različitih eksperimenata na kojima vidimo koliko dobro radi prilagodba domene nad više domena.
Abstract (english)
Today there is a trend of ever increasing use of machine learning for different problems. One of these problems is natural language processing where machine learning achieves very good results. In order to be able to use machine learning, we need data, and although we have more data than ever before, we need well-labeled data. Domain adaptation can help us with this problem. In this paper, we explain what is machine learning, which is domain adaptation, and how it can help us in the sentiment analysis for different domains. Then we consider the practical part of this work: data preparation, model selection and performing different experiments to see how well does domain adaptation work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:457933
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-13 21:27:48