Based on ParlaMint data, researchers Bojan Evkoski and Senja Pollak developed and then explained machine learning models in order to understand the language used by Slovenian members of parliament associated with different political leanings during the migrant crisis from 2014-2020.
With the models showing great predictive success, the researchers then used explainability techniques in order to identify the key words and phrases that have the strongest influence on predicting the political leaning on the topic. The researchers argue that understanding the reasoning behind predictions can be helpful as a tool for qualitative analysis in interdisciplinary research.
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