Strojno učenje za analizu sentimenta temeljenu na aspektima

Zec, Mario

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undergraduate thesis

Strojno učenje za analizu sentimenta temeljenu na aspektima

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:693800

Zec, Mario

University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

APA 6th Edition

Zec, M. (2020). Strojno učenje za analizu sentimenta temeljenu na aspektima (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:693800

MLA 8th Edition

Zec, Mario. "Strojno učenje za analizu sentimenta temeljenu na aspektima." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:693800

Chicago 17th Edition

Harvard

Zec, M. (2020). 'Strojno učenje za analizu sentimenta temeljenu na aspektima', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 10 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:693800

Vancouver

Zec M. Strojno učenje za analizu sentimenta temeljenu na aspektima [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 July 10] Available at: https://urn.nsk.hr/urn:nbn:hr:168:693800

IEEE

M. Zec, "Strojno učenje za analizu sentimenta temeljenu na aspektima", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:693800

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:693800

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Metadata

Title	Strojno učenje za analizu sentimenta temeljenu na aspektima
Title (english)	Machine Learning for Aspect-Based Sentiment Analysis
Author	Mario Zec
Mentor	Jan Šnajder (mentor)
Committee member	Jan Šnajder (predsjednik povjerenstva)
Committee member	Domagoj Alagić (član povjerenstva)
Committee member	Zoran Medić (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing Zagreb
Defense date and country	2020-07-06, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Abstract	Tema ovoga rada je analiza sentimenta temeljena na aspektima. Korišten je skup podataka s natjecanja Semeval iz 2015. godine za zadatak 12. Prva tri modela ovoga rada omogućila su nam bolji uvid u sami problem i dodatne informacije o skupu podataka. U njma su isprobani pristupi koji koriste koeficijent Sørensen-Dice, te jednoslojni i dvoslojni modeli modela CRF. Konačno rješenje temeljili smo na pretpostavci da svakome aspektu mora biti pripisan jedan dio rečenice. Ako to nije slučaj, onda dolazimo do problema s označavanjem skupa podataka jer ako jedna jednostavna rečenica sadrži dva različita aspekta onda nije jasno koji aspekt se odnosi na koji dio rečenice. Analizom skupa podataka i algoritamskim rastavljanjem rečenica uvjerili smo se da je moguće samo veznicima i interpunkcijskim znakovima rastvljati rečenice s uspješnošću od 98%, gdje broj dijelova rastavljene rečenice odgovara broju aspekata iz skupa podataka. Temeljem ove ideje izgrađen je model koji je rastavljao rečenice algoritamski te koristio strojno učenje za predviđanje aspekata na temelju dijelova rečenica. Uspješnost modela je zadovoljavajuća, ali ostaje velik prostor za poboljšanje modela.
Abstract (english)	This thesis is about aspect-based sentiment analysis (ABSA). The dataset of Semeval 2015 for task 12 was used. First three models gave us better view of the task and additional informations about dataset. Approaches that were used are: Sørensen-Dice coefficient and one and two layers CRF models. The fourth model was based on an assumption that every aspect belongs to one part of the sentence. If that is not true then we come to the problem that annotations are fuzzy. Based on dataset analysis and algorithmic partitioning of sentences we get that we can partition 98% of sentences correct using conjunctions and punctuation marks so that number of aspects is equal to the number of sentence parts. Based on this idea we build a model that algorithmically partitions sentences and then using machine learning predicts aspects based on sentence parts. Success of this model is satisfactory but there is room for improvements.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:693800
Study programme	Title: 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 resource	Text
File origin	Born digital
Access conditions	Closed access
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Repository	FER Repository
Created on	2023-01-14 10:18:55