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master's thesis
Model za analizu sentimenta u tvitovima na hrvatskome jeziku
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. urn:nbn:hr:168:817186
Dolović, Dino
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Dolović, D. (2015). Model za analizu 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:168:817186

Dolović, Dino. "Model za analizu sentimenta u tvitovima na hrvatskome jeziku." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:817186

Dolović, Dino. "Model za analizu sentimenta u tvitovima na hrvatskome jeziku." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:817186

Dolović, D. (2015). 'Model za analizu sentimenta u tvitovima na hrvatskome jeziku', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:817186

Dolović D. Model za analizu sentimenta u tvitovima na hrvatskome jeziku [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2015 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:817186

D. Dolović, "Model za analizu sentimenta u tvitovima na hrvatskome jeziku", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:168:817186

Metadata
TitleModel za analizu sentimenta u tvitovima na hrvatskome jeziku
Title (english)Sentiment Analysis in Tweets in Croatian Language
AuthorDino Dolović
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberSlobodan Ribarić (član povjerenstva)
Committee memberVlado Glavinić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2015-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Blogovi, recenzije proizvoda, korisnički generirane poruke poput tvitova i ostali pisani tekstovi ogroman su izvor informacija i znanja. Takvi tekstovi i poruke u velikoj većini slučajeva iznose mišljenja i stavove autora o nečemu ili nekome. Agregacijom takvih mišljenja te njihovom automatskom obradom, odnosno analizom, uvelike se može doprinijeti osobama od interesa, najčešće poslovnim entitetima. Analiza sentimenta ili mišljenja zadatak je iz područja obrade prirodnog jezika te je predmet ovog diplomskog rada. U okviru ovog diplomskog rada proučeni su postupci za analizu sentimenta u mikroblogovima, s naglaskom na metode temeljene na nadziranom strojnom učenju. Provedeno je označavanje određenog skupa podataka, kojeg čine tvitovi koji se odnose na domenu dvaju pjevačkih emisija: The Voice te X Factor Adria. Nadalje, razvijen je model za analizu sentimenta u tvitovima na hrvatskome jeziku temeljen na nadziranom strojnom učenju koji omogućuje klasifikaciju tvita na razini cijele poruke. Provedeno je iscrpno vrednovanje na odgovarajućem skupu podataka, uključujući i analizu značajki. Dobiveni rezultati usporedivi su s rezultatima dobivenim na natjecanju SemEval-2013, uz postignutu F-micro mjeru od 87%, odnosno F-macro od 71%.
Abstract (english)
Blogs, product reviews, user generated content such as tweet messages and other written texts are huge source of information and knowledge. Such texts and messages are full of users' opinions and their attitudes towards something or someone. One can significantly contribute to the entities that were mentioned, which are mostly in business related domains, by aggregating and automatically analysing such opinions and attitudes. Sentiment analysis is a task in natural language processing and is studied in this thesis. In this research, certain approaches have been studied that consider sentiment analysis in microblogs, focusing on supervised machine learning techniques. A sample of data was annotated, which consisted of tweets in Croatian about two popular music shows: The Voice and X Factor Adria. A system based on the supervised machine learning technique was implemented in order to classify the tweet into one possible sentiment class considering the whole tweet message. The implemented system was then evaluated and yielded results that could be measured with those achieved at SemEval-2013 competition, producing the F-micro of 87% and the F-macro of 71%.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:817186
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
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Created on2018-02-06 08:26:37