Research Article | Open Access | https://doi.org/10.55640/corr-v02i04-01

ANALYSIS OF UZBEKISTAN'S RELATIONS WITH CHINA, RUSSIA, AND SOUTH KOREA: UTILIZING TEXT MINING BASED ON GDELT BIG DATA

Abstract

GDELT (Global Dataset on Events, Language, and Tone), a comprehensive event dataset developed in 2013, provides quantifiable data on cooperative and conflictual relations between countries, as well as events related to specific phenomena. Its utility has been widely recognized, and it is actively used in international relations and foreign policy research. This study also utilizes GDELT data to introduce a method for analyzing the relationships between Uzbekistan and China, Russia, and South Korea through keyword correlation indicators derived from text mining, and to present the actual analysis results. By conducting a keyword correlation analysis based on event data between Uzbekistan and the three analyzed countries—China, Russia, and South Korea—it was possible to identify diplomatic action patterns through the correlation of core keywords such as "engage," "express intent," and "make" with keywords indicating specific actions. Uzbekistan’s foreign policy under the Mirziyoyev government has shown a diplomatic action pattern of seeking or participating in negotiations, expressing intentions for negotiation or cooperation, and pursuing meetings for negotiation and cooperation with the analyzed countries, while inviting or visiting counterpart countries as part of these processes. This demonstrates that Uzbekistan has actively worked towards establishing cooperative relations, which were set as the goal or direction of its foreign relations, named the “Good Neighbor Policy” by President Mirziyoyev. Such keyword correlation indicator analysis facilitates the explanation of cooperative relationships.

Keywords

Relations, Big Data, GDELT, CAMEO

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ANALYSIS OF UZBEKISTAN’S RELATIONS WITH CHINA, RUSSIA, AND SOUTH KOREA: UTILIZING TEXT MINING BASED ON GDELT BIG DATA. (2024). Critique Open Research & Review, 2(04), 01-10. https://doi.org/10.55640/corr-v02i04-01