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master's thesis
Usporedba pristupa analizi sentimenta
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:361308
Vesinger, Dunja
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Vesinger, D. (2019). Usporedba pristupa analizi sentimenta (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:361308

Vesinger, Dunja. "Usporedba pristupa analizi sentimenta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:361308

Vesinger, Dunja. "Usporedba pristupa analizi sentimenta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:361308

Vesinger, D. (2019). 'Usporedba pristupa analizi sentimenta', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 01 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:361308

Vesinger D. Usporedba pristupa analizi sentimenta [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 June 01] Available at: https://urn.nsk.hr/urn:nbn:hr:168:361308

D. Vesinger, "Usporedba pristupa analizi sentimenta", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:361308

Metadata
TitleUsporedba pristupa analizi sentimenta
Title (english)Comparison of Sentiment Analysis Approaches
AuthorDunja Vesinger
MentorMarko Čupić (mentor)
Committee memberMarko Čupić (predsjednik povjerenstva)
Committee memberVlado Glavinić (član povjerenstva)
Committee memberAlan Jović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad proučava različite pristupe rješavanja problema binarne analize sentimenta. Njezin je zadatak odrediti je li sentiment izražen u nekom tekstualnom dokumentu pozitivan ili negativan. Uspoređeni su različiti modeli strojnog učenja koji koriste reprezentacije dokumenata tf-idf te oni koji se služe vektorskim reprezentacijama riječi kako bi predstavili dokumente. Performanse modela mjerene su na četiri različita skupa podataka koja su sastavljena od korisničkih recenzija. Pokazuje se da su na manjim skupovima podataka uspješniji tradicionalni pristupi koji koriste stroj potpornih vektora i reprezentacije tf-idf, dok se na većim skupovima podataka bolje pokazuju konvolucijske neuronske mreže koje koriste vektorske reprezentacije riječi.
Abstract (english)
This paper studies different approaches to binary sentiment analysis. Its task is to determine whether a textual document expresses positive or negative sentiment. Different machine learning models were compared using tf-idf document representations as well as word embeddings. The model performance was measured using four different datasets compound of user reviews. It is shown that traditional approaches which use support vector machines and tf-idf representations are more successful on smaller datasets. Convolutional neural networks that use word embeddings performed better on larger datasets.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:361308
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2020-01-07 22:48:40