Abstract :
This paper continues to test the ASSANA methodology for the computer-assisted analysis of large-scale, unstructured, text-based data in international affairs research. Traditional methodologies for exploring the role of the Secretary of State in projecting "soft power" have focused on reading and analyzing texts to uncover important themes and patterns. However, the increasingly large volume of data available presents a significant challenge for these traditional methods of analysis. We examine three data sets consisting of every remark by Secretary Clinton since taking office (n=2, 438), Secretary Rice from 2005-2009 (n=1, 766), and Secretary Albright from 1997-2001 (n=1, 335). We use computer-assisted content analysis to find key themes for each Secretary and search for similarities between key themes and phrases. We find a limited number of similar keywords across the dataset, except International and Issues. However, the following phrases are shared between all Secretaries: Human Rights, Foreign Policy, Middle East, International Community, and North Korea.
Keywords :
content management; data analysis; public administration; public information systems; text analysis; ASSANA methodology; Foreign Policy; Human Rights; International Community; Middle East; North Korea; Secretary of State; US Secretaries of State; computationally intensive content analysis; computer-assisted content analysis; data sets; international affairs research; large-scale unstructured text-based data; public diplomacy data; soft power; Computers; Data analysis; Educational institutions; Government; HTML; Software; Speech; Big Data; Computational Analysis; Public Diplomacy;
