prikaz prve stranice dokumenta Analiza sentimenta u muzičkim žanrovima
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master's thesis
Analiza sentimenta u muzičkim žanrovima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:881000
Čagalj, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Čagalj, L. (2020). Analiza sentimenta u muzičkim žanrovima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:881000

Čagalj, Luka. "Analiza sentimenta u muzičkim žanrovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:881000

Čagalj, Luka. "Analiza sentimenta u muzičkim žanrovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:881000

Čagalj, L. (2020). 'Analiza sentimenta u muzičkim žanrovima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:168:881000

Čagalj L. Analiza sentimenta u muzičkim žanrovima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:881000

L. Čagalj, "Analiza sentimenta u muzičkim žanrovima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:881000

Metadata
TitleAnaliza sentimenta u muzičkim žanrovima
Title (english)Sentiment Analysis of Music Genres
AuthorLuka Čagalj
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberMarin Šilić (član povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-09-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Analiza sentimenta ostvarena je na više načina. Različite reprezentacije teksta poput TF-IDF reprezentacije, reprezentacije dobivene primjenom rangirajuće funkcije BM25 i GloVe modela za učenje vektorskih reprezentacija riječi, koristile su se za učenje modela. U izradi sustava koristili su se modeli: logistička regresija, stroj potpornih vektora i LSTM povratna neuronska mreža. Za problem binarne klasifikacije sentimenta dobivena je točnost 78\%, dok je za višeklasni problem dobivena točnost 60\% na skupu za testiranje. Zbog nedostatka označenih podataka korišteno je aktivno učenje modela. Naučeni modeli su korišteni u analizi sentimenta unutar glazbenih žanrova.
Abstract (english)
The analysis of sentiment has been accomplished in a number of ways. Various text representations such as TF-IDF representation, representation obtained by applying the BM25 ranking function, and GloVe model for learning vector word representations were used to train models. Models were used in the design of the system: logistic regression, support vector machine and LSTM recurrent neural network. Accuracy of 78 \% was obtained for binary classification and for a multiclass problem, 60 \% accuracy was obtained on the test set. Active learning was used due to the lack of labeled data. Learned models were used in sentiment analysis within musical genres.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:881000
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 on2023-01-13 11:59:22