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master's thesis
Otkrivanje govora mržnje zasnovano na modelima strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:534922
Šmuc, Eva
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šmuc, E. (2022). Otkrivanje govora mržnje zasnovano na modelima strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:534922

Šmuc, Eva. "Otkrivanje govora mržnje zasnovano na modelima strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:534922

Šmuc, Eva. "Otkrivanje govora mržnje zasnovano na modelima strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:534922

Šmuc, E. (2022). 'Otkrivanje govora mržnje zasnovano na modelima strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:534922

Šmuc E. Otkrivanje govora mržnje zasnovano na modelima strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 July 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:534922

E. Šmuc, "Otkrivanje govora mržnje zasnovano na modelima strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:534922

Metadata
TitleOtkrivanje govora mržnje zasnovano na modelima strojnog učenja
Title (english)Hate speech detection based on machine learning models
AuthorEva Šmuc
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberMarin Šilić (član povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-09-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
U ovom radu, odnos govora mržnje i sličnih zadataka obrade prirodnog jezika istražen je kroz tehniku prijenosa učenja. Skup podataka ETHOS odabran je kao ciljni skup podataka za klasifikaciju. Nekoliko je algoritama trenirano za srodne zadatke (npr. toksični govor) na većim skupovima podataka, a dobiveni modeli su iskorišteni za dobivanje predikcija za ciljni skup podataka. Predikcije su korištene kao jedine ili dodatne značajke u eksperimentima koji slijede. Eksperimenti se provode korištenjem više algoritama (Logistic Regression, SVM, RidgeClassifier, Decision Tree, Random Forest, AdaBoost, GradBoost, Bagging) i višestrukih prikaza teksta, uključujući Tf-Idf, Bert embeddinge i Bert embeddinge u kombinaciji sa spomenutim dodatnim značajkama. Modeli temeljeni na transformerima, BERT i DistilBERT koriste se za fino podešavanje na skupu podataka ETHOS. Svi dobiveni modeli su evaluirani i njihove metrike su međusobno uspoređene i uspoređene s rezultatima koje su dobili autori skupa podataka ETHOS. Kako bi se istražili preostali problemi, koristi se model-agnostička metoda LIME za dobivanje objašnjenja za pojedine pogrešne predikcije.
Abstract (english)
In this work, the relationship between hate speech and similar tasks is explored through application of transfer learning techniques. A hate speech dataset called ETHOS is chosen as the target dataset. Multiple algorithms are trained for similar tasks (e.g. toxic language detection) on larger datasets, and the obtained models are used for getting predictions for the target dataset. These predictions are used as sole or additional features in the experiments that follow. Experiments are carried out using multiple algorithms (Logistic Regression, SVM, RidgeClassifier, Decision Tree, Random Forest, AdaBoost, GradBoost, Bagging) and multiple text representations, including Tf-Idf, Bert embeddings and Bert embeddings combined with mentioned additional features. Transformer-based models BERT and DistilBERT are introduced and fine-tuned on ETHOS dataset. All the obtained models are evaluated and their performance metrics is compared mutually and compared to results obtained by the authors of the ETHOS dataset. In order to explore the remaining underlying issues, model-agnostic method LIME is used to get explanations for incorrect predictions.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:534922
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:20:20