Using Machine Linguistics to Analyze Trade and Economic News

Andrey Nikolaevich Spartak, Ivan Nikolaevich Oleynikov, Alexander Alekseevich Shatilov and Fedor Mikhailovich Yaronskiy
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Andrey Nikolaevich Spartak: Russian Market Research Institute, Moscow, Russia
Ivan Nikolaevich Oleynikov: Russian Foreign Trade Academy, Moscow, Russia
Alexander Alekseevich Shatilov: Russian Foreign Trade Academy, Moscow, Russia
Fedor Mikhailovich Yaronskiy: Russian Foreign Trade Academy, Moscow, Russia

Russian Foreign Economic Journal, 2022, issue 12, 55-67

Abstract: Monitoring international trade and economic news, selecting the relevant ones and quickly analyzing the current state of the world economy is an important activity of economic departments. However, it is practically impossible to manually collect the necessary articles from a huge number of online sources, to visually check and then categorize them. The purpose of this paper is to show the stages of collection, preparation of news texts for processing, markup and further classification of trade and economic news using machine linguistics models, to describe which soft ware tools and modeling approaches were used in the paper, and to demonstrate the results practical application of the built system.

Keywords: natural language processing; text analysis information system; trade and economic news; model; parsing; markup; data collection; model training; database (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:alq:rufejo:rfej_2022_12_55-67

DOI: 10.24412/2072-8042-2022-12-55-67

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