Economic Activity Forecasting Based on the Sentiment Analysis of News

Mantas Lukauskas (), Vaida Pilinkienė, Jurgita Bruneckienė, Alina Stundžienė, Andrius Grybauskas and Tomas Ruzgas
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Mantas Lukauskas: Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 44249 Kaunas, Lithuania
Vaida Pilinkienė: School of Economics and Business, Kaunas University of Technology, 44249 Kaunas, Lithuania
Jurgita Bruneckienė: School of Economics and Business, Kaunas University of Technology, 44249 Kaunas, Lithuania
Alina Stundžienė: School of Economics and Business, Kaunas University of Technology, 44249 Kaunas, Lithuania
Andrius Grybauskas: School of Economics and Business, Kaunas University of Technology, 44249 Kaunas, Lithuania
Tomas Ruzgas: Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 44249 Kaunas, Lithuania

Mathematics, 2022, vol. 10, issue 19, 1-22

Abstract: The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic activity is characterized by various indicators such as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation, and other frequently used economic indicators. The most popular were the gross domestic product (GDP) and industrial production. However, such traditional tools have started to decline in modern times (as the timely knowledge of information becomes a critical factor in decision making in a rapidly changing environment) as they are published with significant delays. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to assess economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to forecast traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse and in this case the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.

Keywords: clustering; economic activity; natural language processing; NLP; transformers; BERT; forecasting; nowcasting; economic sentiment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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