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Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings
[article]
2017
arXiv
pre-print
Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads of state or government in the United Nations General Debate. The paper provides three key contributions based on the output of the Word2vec model. First, it presents a set of
arXiv:1707.03490v1
fatcat:jt7m6wzjvva6rfru3wycze7rku