Natural Language Processing-Driven Use-Cases for Economic Analysis Using Unstructured Data

Temesvári, Csanád and Horváth, Beáta and Ónozó, Lívia Réka (2026) Natural Language Processing-Driven Use-Cases for Economic Analysis Using Unstructured Data. FINANCIAL AND ECONOMIC REVIEW, 25 (1). pp. 27-52. ISSN 2415-9271 (print); 2415–928X (online)

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Abstract

Economic text data, such as news articles or retail trade item names, are an alternative, feature-rich, high frequency information source that can provide insight into economic trends and generate timelier and more accurate estimates. We trained multiple deep learning models for two distinct research tasks: 1) the creation of a sentiment index derived from the categorisation of financial and economic articles into three sentiment categories; and 2) the classification of retail trade item names into appropriate tariff categories. Our models consistently outperformed their baseline counterparts for retail trade item classification, while our sentiment index was able to accurately predict economic downturns where high-frequency data were not available.

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