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undergraduate thesis
Primjena modela dubokog učenja za analizu sentimenta
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. urn:nbn:hr:168:737684
Gavranović, Bruno
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Gavranović, B. (2016). Primjena modela dubokog učenja za analizu sentimenta (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:737684

Gavranović, Bruno. "Primjena modela dubokog učenja za analizu sentimenta." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:737684

Gavranović, Bruno. "Primjena modela dubokog učenja za analizu sentimenta." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:737684

Gavranović, B. (2016). 'Primjena modela dubokog učenja za analizu sentimenta', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:737684

Gavranović B. Primjena modela dubokog učenja za analizu sentimenta [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2016 [cited 2024 November 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:737684

B. Gavranović, "Primjena modela dubokog učenja za analizu sentimenta", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:168:737684

Metadata
TitlePrimjena modela dubokog učenja za analizu sentimenta
Title (english)Application of Deep Learning for Sentiment Analysis
AuthorBruno Gavranović
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberDomagoj Alagić (član povjerenstva)
Committee memberMartin Tutek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2016-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Uzevši u obzir eksponencijalni rast dostupnih podataka generiranih u cijelom svijetu, postoji rastući interes za stvaranje modela sposobnih za analizu podataka koji imaju semantički nepoznat kontekst. Ovaj rad se fokusira na istraživanje i analizu teorijske osnove koja stoji iza modela zvanih povratne neuronske mreže (engl. recurrent neural networks, RNN) kroz konkretnu implementaciju na problemu semantičke analize. Poseban slučaj povratnih neuronskih mreža,mreže s dugotrajnim kratkoročnim pamćenjem(engl. Long short-term memory networks, LSTM) su implementirane na problemu klasifikacije sentimenata na skupu podataka recenzija filmova. Testirane su i uspoređene mnogobrojne arhitekture i konfiguracije hiperparametara spomenutih neuronskih mreža. Značajano poboljšanje performansi je primjećeno s dubokim višeslojnim LSTM mrežama, u za razliku od rezultata dobivenih s plitkim mrežama.
Abstract (english)
Given the exponential growth of amount of available data generated worldwide, there has been a rising interest in the creation of models capable of analyzing the data without knowing the semantic context. This paper focuses on exploration and analysis of the theoretical foundation that stands behind models called recurrent neural networks through concrete implementation on a problem of semantic analysis. Special case of recurrent neural networks,long short-term memory networks (LSTM networks) were implemented on a task of semantic classification on the IMDb dataset. Various architectures and hyperparameter configurations were tested and compared on the said neural networks. Significant performance boost was detected with deep multi-layer LSTM networks, compared to performance with shallow ones.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:737684
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 on2018-02-06 08:52:08