Abstract: In the first stage, we pose a testable hypothesis: “How do democracies and autocracies frame the principle of sovereignty differently?” Sovereignty is the most fundamental legal principle in the realm of global governance, developed to guarantee legally equal status among states and respect authority over territories. However, authoritarian states have invoked the sovereignty principle, framing it as a free pass to enact any policies domestically. We aim to determine if there is any systematic difference in rhetorical usage between the two types of regimes, using text analysis models. We use pre-trained static and dynamic models like GloVe and BERT to generate word-embeddings for each document.In the next stage, we analyze not only what the leaders say, but how they speak by employing computational linguistics models. Our goal is to unpack the preferences of authoritarian state leaders by mapping UNGD data to psychological markers. Linguistic Inquiry and Word Count (LIWC) generates dictionary-based measures of constructs that tap into linguistic styles. Using simple regression and random forest model, our prediction model hit 75% accuracy level of predicting regime type using linguistic features. This project is expected to contribute to the timely discussion on the growing political clout of authoritarian regimes.