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Evaluation of news sentiment in economic activity forecasting
* 1, 2 , 3 , 3 , 3 , 3 , 4
1  Kaunas University of Technology
2  Zyro Inc
3  School of Economics and Business, Kaunas University of Technology, 44249 Kaunas, Lithuania
4  Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 44249 Kaunas, Lithuania;
Academic Editor: Nunzio Cennamo


Currently, artificial intelligence is getting more and more different applications in practice. One of the areas of artificial intelligence that has seen significant improvement in recent years is natural language processing. Natural language processing is a discipline with characteristics of linguistics and computer science. This field studies applying various mathematical and computational methods to natural language processing. The application areas can be diverse and include text reading and voicing, automatic translation, which everyone often uses, automatic text correction, information search and many other areas. This can be applied in economic process forecasting as well. Most often, the country's economic activity is characterised by such indicators 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. 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 evaluate 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 perform forecasting of 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 as 8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.

Keywords: clustering; economic activity; natural language processing; NLP; sentiment analysis; forecasting; nowcasting