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master's thesis
Model analize sentimenta temeljen na n-gramima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:171027
Leontić, Fran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Leontić, F. (2023). Model analize sentimenta temeljen na n-gramima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:171027

Leontić, Fran. "Model analize sentimenta temeljen na n-gramima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:171027

Leontić, Fran. "Model analize sentimenta temeljen na n-gramima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:171027

Leontić, F. (2023). 'Model analize sentimenta temeljen na n-gramima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:171027

Leontić F. Model analize sentimenta temeljen na n-gramima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 December 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:171027

F. Leontić, "Model analize sentimenta temeljen na n-gramima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:171027

Metadata
TitleModel analize sentimenta temeljen na n-gramima
Title (english)N-gram based sentiment analysis model
AuthorFran Leontić
MentorMarko Horvat (mentor)
Committee memberMarko Horvat (predsjednik povjerenstva)
Committee memberMirjana Domazet-Lošo (član povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-07, Croatia
Abstract
Diplomski rad bavi se primjenom analize sentimenta i emocija na tekstove na hrvatskom jeziku koristeći pristup temeljen na n-gramima, tj. pristup temeljen na leksikonu. U sklopu rada izrađeni su modeli za ocjenjivanje tekstova koji se temelje na dimenzijskom modelu emocija i diskretnom modelu emocija. Ocijenjeni su tekstualni dokumenti od kasne 2019. godine do početka 2021. godine, koji su značajni jer predstavljaju razdoblje prva dva vala COVID-19 pandemije u Hrvatskoj. Iz ocijenjenih tekstova pomoću dimenzijskog modela zaključeno je da je pandemija izazvala neugodu, da su emocije bile niskog intenziteta i da ljudi nisu imali osjećaj kontrole nad emocijama. Iz ocijenjenih tekstova pomoću diskretnog modela zaključeno je da su prevladavajuće emocije strah i tuga. Izrađena je web-aplikacija koja demonstrira primjenu navedenih modela na proizvoljne tekstove.
Abstract (english)
This thesis focuses on the application of sentiment analysis and emotion analysis on Croatian texts using an n-gram or lexicon based approach. The developed models for text evaluation are based on the dimensional and discrete emotion models. Text documents from late 2019. to early 2021. were evaluated. They are significant because they represent the first two COVID-19 pandemic waves in Croatia. The dimensional model's ratings show that the pandemic caused discomfort, that the emotions were of low intensity and that the people did not have control over their emotions. The discrete model's ratings show that the prevailing emotions were fear and sadness. A web-application that demonstrates the usage of the mentioned models on user inputs was developed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:171027
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 on2023-10-11 09:32:06