Understanding parliamentary discourse and broader political debates is essential for comprehending the political processes and decisions that impact society. The thesis addresses the challenge of machine learning-based identification and analysis of the stances of parliament members and their parties on various topics using a three-class classification: 'for,' 'against,' and 'neutral.' The analysis includes comparisons of stances in the Serbian parliament. We utilized a manually annotated dataset containing 1,019 examples for the analysis. We evaluated several language models, such as XML-RoBERTa, BERTić, POLITICS, YugoGPT, and Llama-3.1, and compared their performance. Our findings confirm the general knowledge of political parties and their orientations, demonstrating the capability of large language models to analyze large datasets.
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