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Analysis and Filtration of User-generated Content in Web Development

Student: Vladislava Smirnova

Supervisor: Boris Orekhov

Faculty: Faculty of Humanities

Educational Programme: Computational Linguistics (Master)

Final Grade: 10

Year of Graduation: 2021

The aim of the study was solving three problems of analyzing and filtering user-generated text content. The list of the covered tasks consisted of obscene words detection, advertising detection and toxicity detection. As a part of the thesis modern approaches to these problems were analyzed. Experiments with implementation and evaluation of classical machine learning algorithms and neural networks were conducted. All the implemented neural models not only surpassed other machine learning algorithms, but also reached and in one case exceeded the state-of-the-art f-score and accuracy values for each task. The result of the work was realization of the open library for analyzing and filtering user-generated texts.

Full text (added May 31, 2021)

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