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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sofi a Alexeevna OSOKINA & Victoria Leonidovna ABRAMOVA & Daria Andreevna LYUTOVA, 2025. "Analyzing the Sentiment of international Trade News in the Context of Sanctions: NLP Approaches," Russian Foreign Economic Journal, Russian Foreign Trade Academy Ministry of economic development of the Russian Federation, issue 2, pages 77-93, February.
    • Handle: RePEc:alq:rufejo:rfej_2025_02_77-93
    DOI: 10.24412/2072-8042-2025-2-77-93
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    More about this item

    Keywords

    Natural Language Processing; NLP; machine linguistics; artificial intelligence; sanctions; sentiment analysis; trade news; international trade;
    All these keywords.

    JEL classification:

    • F10 - International Economics - - Trade - - - General

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