Analyzing the Sentiment of international Trade News in the Context of Sanctions: NLP Approaches

Sofi a Alexeevna Osokina, Victoria Leonidovna Abramova and Daria Andreevna Lyutova
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Sofi a Alexeevna Osokina: Russian Foreign Trade Academy, Moscow, Russia
Victoria Leonidovna Abramova: Russian Foreign Trade Academy, Moscow, Russia
Daria Andreevna Lyutova: Russian Foreign Trade Academy, Moscow, Russia

Russian Foreign Economic Journal, 2025, issue 2, 77-93

Abstract: The article focuses on exploring the characteristics of natural language processing (NLP) in trade sanctions-related news. Emphasis is placed on identifying lexical and structural features of texts that can affect the quality of automated analysis. The importance of considering context and cultural differences when evaluating the tone of news is highlighted, along with discussing challenges associated with interpreting economic and political content. An overview of contemporary sentiment analysis methods, including approaches based on machine learning and neural networks, is presented. Practical aspects of applying these methods to analyze sanctionrelated news, taking into account their specificities and ambiguities, are also discussed.

Keywords: Natural Language Processing; NLP; machine linguistics; artificial intelligence; sanctions; sentiment analysis; trade news; international trade (search for similar items in EconPapers)
JEL-codes: F10 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:alq:rufejo:rfej_2025_02_77-93

DOI: 10.24412/2072-8042-2025-2-77-93

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