prikaz prve stranice dokumenta Analiza mnijenja u podatkovnom skupu SentiGMapsCro
  Download
PDF 2.31 MB
master's thesis
Analiza mnijenja u podatkovnom skupu SentiGMapsCro
Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences, 2024. urn:nbn:hr:131:991862
Gašparić, Brigita
University of Zagreb
Faculty of Humanities and Social Sciences
Department of information and Communication sciences

Institutional repository: ODRAZ

Cite this document

Gašparić, B. (2024). Analiza mnijenja u podatkovnom skupu SentiGMapsCro (Master's thesis). Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences. Retrieved from https://urn.nsk.hr/urn:nbn:hr:131:991862

Gašparić, Brigita. "Analiza mnijenja u podatkovnom skupu SentiGMapsCro." Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, 2024. https://urn.nsk.hr/urn:nbn:hr:131:991862

Gašparić, Brigita. "Analiza mnijenja u podatkovnom skupu SentiGMapsCro." Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, 2024. https://urn.nsk.hr/urn:nbn:hr:131:991862

Gašparić, B. (2024). 'Analiza mnijenja u podatkovnom skupu SentiGMapsCro', Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, accessed 27 September 2024, https://urn.nsk.hr/urn:nbn:hr:131:991862

Gašparić B. Analiza mnijenja u podatkovnom skupu SentiGMapsCro [Master's thesis]. Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences; 2024 [cited 2024 September 27] Available at: https://urn.nsk.hr/urn:nbn:hr:131:991862

B. Gašparić, "Analiza mnijenja u podatkovnom skupu SentiGMapsCro", Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:131:991862

Metadata
TitleAnaliza mnijenja u podatkovnom skupu SentiGMapsCro
Title (english)Sentiment analysis in the SentiGMapsCro data set
AuthorBrigita Gašparić
MentorNives Mikelić Preradović (mentor)
Committee memberTomislava Lauc (predsjednik povjerenstva)
Committee memberSanja Kišiček (član povjerenstva)
Committee memberNives Mikelić Preradović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Humanities and Social Sciences
(Department of information and Communication sciences)
Zagreb
Defense date and country2024-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
Organization and Informatics
Abstract
Ovaj diplomski rad istražuje analizu sentimenta na temelju aspekta unutar podatkovnog skupa SentiGMapsCro koji je nastao sakupljanjem najbolje i najgore recenziranih mjesta i objekata na području Grada Zagreba u Hrvatskoj s internetske stranice https://www.top-rated.online. Jezik, temeljni element ljudske komunikacije, prenosi informacije, emocije i mnijenje. Ovaj rad naglašava važnost analize sentimenta u razumijevanju subjektivnih iskustava podijeljenih na Internetu. Opisuje se postupak ručnog označavanja podatkovnog skupa SentiGMapsCro unutar alata za označavanje pod nazivom INCEpTION, s oznakama sentimenata u rasponu od -2 (vrlo negativan sentiment) do +2 (vrlo pozitivan sentiment) za obuku modela strojnog učenja. Rad naglašava izazove u analizi sentimenta, kao što su složenost prirodnog jezika, prisutnost ironije i metafora, problemi segmentacije te gramatičke pogreške. U radu se ocrtavaju teoretski okviri i istraživačke metodologije korištene u procesu označavanja, ilustrirajući poteškoće s kojima se istraživači susreću u računalnoj obradi prirodnog jezika.
Abstract (english)
The thesis explores sentiment analysis based on aspect within the SentiGMapsCro dataset, focusing on the best and worst reviews of places in Zagreb, Croatia extracted from https://www.top-rated.online. Language, a fundamental element of human communication, conveys information, emotions, and opinions. The study emphasizes the importance of sentiment analysis in understanding subjective experiences shared online. It describes the manual labeling process of the dataset within the annotation tool called INCEpTION, with sentiments ranging from -2 (very negative sentiment) to +2 (very positive sentiment), to train machine learning models. The thesis highlights challenges in sentiment analysis, such as natural language complexities and the presence of irony, metaphors, segmentation issues, and grammatical errors. It outlines theoretical frameworks and research methodologies employed in the labeling process, illustrating the difficulties encountered in computer-based natural language processing.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:131:991862
Study programmeTitle: Information Sciences; specializations in: Archivistics Study, Library Science, Information Sciences (research), Information Sciences (teaching), Museology and heritage management, Informatology
Course: Information Sciences (teaching)
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra informacijskih znanosti (magistar/magistra informacijskih znanosti)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-17 15:43:58