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master's thesis
Analiza sentimenta u tvitovima na hrvatskome jeziku
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. urn:nbn:hr:168:908184
Krajcar, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Krajcar, L. (2014). Analiza sentimenta u tvitovima na hrvatskome jeziku (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:908184

Krajcar, Luka. "Analiza sentimenta u tvitovima na hrvatskome jeziku." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:908184

Krajcar, Luka. "Analiza sentimenta u tvitovima na hrvatskome jeziku." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:908184

Krajcar, L. (2014). 'Analiza sentimenta u tvitovima na hrvatskome jeziku', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 14 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:908184

Krajcar L. Analiza sentimenta u tvitovima na hrvatskome jeziku [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2014 [cited 2025 January 14] Available at: https://urn.nsk.hr/urn:nbn:hr:168:908184

L. Krajcar, "Analiza sentimenta u tvitovima na hrvatskome jeziku", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:168:908184

Metadata
TitleAnaliza sentimenta u tvitovima na hrvatskome jeziku
Title (english)Sentiment Analysis in Tweets in Croatian Language
AuthorLuka Krajcar
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberMarin Golub (član povjerenstva)
Committee memberNikola Bogunović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2014-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U posljednjih nekoliko godina primjetan je nagli porast korisnički generiranog sadržaja unutar društvenih mreža. Jedna od takvih mreža osobito pogodna za strojnu analizu sentimenta je Twitter, mikro-blogging servis unutar kojega korisnici odašilju kratke poruke - tvitove. Cilj strojne analize sentimenta je automatski odrediti mišljenje, stav ili emociju izraženu u tekstu klasifikacijom u jednu od tri klase: pozitivnu,negativnu ili neutralnu. Zbog nemogućnosti ručnog označavanja velikog broja poruka koristi se model blago nadziranog strojnog učenja. U radu je ispitan utjecaj raznih metoda predobrade podataka te rad nekoliko klasifikacijskih modela poput naivnog Bayesovog klasifikatora, stroja potpornih vektora i logističke regresije. Prikazana je i obrazložena usporedba modela nadziranog i blago nadziranog strojnog učenja. Prikaz agregiranog sentimenta i statistike omogućen je kroz korisničku aplikaciju.
Abstract (english)
In recent years there has been a sharp rise in user-generated content within social networks. One of these networks particularly suitable for machine analysis of sentiment is Twitter, a micro-blogging service within which users broadcast short messages - tweets. The goal of machine sentiment analysis is to automatically determine the opinion, attitude or emotion expressed in text by classification into one of three categories: positive, negative or neutral. Due to the inability of manual labeling of a large number of messages a model of distant supervised machine learning. This paper investigates the impact of different methods of preprocessing data and effectiveness of several classification models such as naive Bayes classifier, support vector machines and logistic regression. Comparison of supervised and distant supervised machine learning is presented and explained in detail. Representation of aggregate sentiment and statistics is provided via a user application.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:908184
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
Access conditionsClosed access
Terms of useLicence In copyright icon
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Created on2018-02-06 09:52:45