prikaz prve stranice dokumenta Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma
No public access
undergraduate thesis
Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:492047
Kurdija, Vedran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Kurdija, V. (2021). Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:492047

Kurdija, Vedran. "Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:492047

Kurdija, Vedran. "Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:492047

Kurdija, V. (2021). 'Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 30 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:492047

Kurdija V. Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 30] Available at: https://urn.nsk.hr/urn:nbn:hr:168:492047

V. Kurdija, "Klasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:492047

Metadata
TitleKlasifikacija sentimenta i emocija u javnim komentarima iz domene telekoma
Title (english)Sentiment and Emotion Classification in Public Comments from the Telecom Domain
AuthorVedran Kurdija
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberMartin Tutek (član povjerenstva)
Committee memberJosip Jukić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Komentari korisnika sadrže neprocjenjive informacije za pružatelje usluga. Značajan dio tih informacija leži u emocijama i sentimentu komentara. Ručna obrada tih komentara skupa je i vremenski zahtjevna. Cilj ovog rada bio je osmisliti i implementirati modele dubokog učenja koji mogu obavljati ovaj zadatak. Provedena je analiza skupa podataka CATACX koji sadrži javne korisničke komentare na hrvatskom jeziku s označenim emocijama i sentimentima. Istražene su korelacije između sentimenata i emocija. Razvijeni su i implementirani modeli strojnog i dubokog učenja: logistička regresija, jedinica za dugotrajno kratkoročno pamćenje (LSTM) i dvosmjerni LSTM. Modeli su upotrijebljeni za klasifikaciju emocija i sentimenata, a ostvarili su značajno bolje rezultate u predviđanju sentimenata, što ukazuje na to da je predviđanje emocija složeniji zadatak.
Abstract (english)
User comments contain invaluable information for service providers. A significant part of this information lies in the emotions and sentiment of the comment. Manual processing of these comments is expensive and time-consuming. The goal of this thesis was to develop and implement deep learning models that can perform this task. An analysis of the CATACX dataset containing public user comments in Croatian with labeled emotions and sentiments was performed, and correlations between sentiments and emotions were investigated. Machine learning and deep learning models were developed and implemented, a logistic regression model, a long short-term memory (LSTM) model, and a bidirectional LSTM model. The models were used to classify emotions and sentiments and performed considerably better in predicting sentiments, suggesting that predicting emotions is a more difficult task.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:492047
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
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2023-01-14 09:14:56