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master's thesis
Postupci prilagodbe domeni za analizu sentimenta u tekstu
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:993949
Paradžik, Matej
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Paradžik, M. (2017). Postupci prilagodbe domeni za analizu sentimenta u tekstu (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:993949

Paradžik, Matej. "Postupci prilagodbe domeni za analizu sentimenta u tekstu." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:993949

Paradžik, Matej. "Postupci prilagodbe domeni za analizu sentimenta u tekstu." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:993949

Paradžik, M. (2017). 'Postupci prilagodbe domeni za analizu sentimenta u tekstu', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:993949

Paradžik M. Postupci prilagodbe domeni za analizu sentimenta u tekstu [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:993949

M. Paradžik, "Postupci prilagodbe domeni za analizu sentimenta u tekstu", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:993949

Metadata
TitlePostupci prilagodbe domeni za analizu sentimenta u tekstu
Title (english)Domain Adaptation for Sentiment Analysis from Text
AuthorMatej Paradžik
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberAnte Đerek (član povjerenstva)
Committee memberLeonardo Jelenković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Uobičajeni pristup izgradnji sustava za analizu sentimenta temelji se na klasifikaciji sentimenta korištenjem algoritama nadziranog strojnog učenja. Da bi takav sustav radio dobro, dokumenti, čiji sentiment će naučeni klasifikator predviđati, trebali bi biti iz iste domene kao i dokumenti na kojima je klasifikator učen. Umjesto skupog označavanja dokumenata za svaku novu domenu, moguće je iskoristiti postojeće označene dokumente iz neke druge domene i primijeniti neki postupak prilagodbe domeni. U ovom radu proučeni su postupci prilagodbe domeni temeljeni na dodjeljivanju težina (engl. instance weighting) i postupci temeljeni na promjeni prostora značajki (engl. change of representation). Proučeni postupci iskorišteni su za prilagodbu domene pri učenju klasifikatora sentimenta temeljenog na stroju potpornih vektora. Postupci prilagodbe domene eksperimentalno su vrednovani pri izgradnji klasifikatora sentimenta za hrvatski i engleski jezik. Kao najbolji postupak za prilagodbu domene pokazala se primjena marginaliziranih odšumljavajućih naslaganih autoenkodera (engl. marginalized denoising stacked autoencoders).
Abstract (english)
Typical sentiment analysis systems are based on supervised classification algorithms which, in order to work well, assume that documents used for training of a classifier come from the same domain as documents sentiment of which classifier will be used to predict. In order to alleviate the need for labeling documents for each new domain of interest, domain adaptation techniques can be used with already available documents from some different domain. This thesis studies several domain adaptation techniques based on instance weighting and change of representation. The studied techniques were used for building domain-adapted sentiment classifiers based on linear support vector machine. The performance of domain-adapted classifiers was experimentally evaluated on texts in Croatian and English languages. Using domain adaptation technique based on marginalized denoising stacked autoencoders gave the best results across all domain adaptation tasks in both languages.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:993949
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 on2019-03-13 23:38:18