The thesis deals with the analysis of hate speech and sentiment in the parliamentary speech of the National Assembly of the Republic of Slovenia. Hate speech is becoming an increasingly pressing social problem, so the purpose of the thesis is to determine to what extent it occurs among MPs at parliamentary sessions and what kind of sentiment is present (positive or negative). We used python programming language and the HuggingFace framework to build several BERT-type machine learning models for hate speech recognition and sentiment detection in parliamentary speech. We used the CroSloEngual BERT model, which was pretrained on Slovenian, Croatian and English language. We found that the use of hate speech between 2016 and 2020 declined in the first four years, but then began to rise sharply in 2020. In the years 2016 to 2018, positive sentiment was expressed more often than negative, and in the next two years, until June 2020, negative sentiment prevailed. The results provide a deeper insight into the extent to which hate speech occurs among the members of the National Assembly of the Republic of Slovenia and what kind of sentiment is present in their speech, which has not been determined by previous studies. The thesis can serve as a starting point for further studies in the field of parliamentary language processing and in the analysis of various aspects of parliamentary speech in Slovenia.
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