Template-Type: ReDIF-Paper 1.0
Author-Name: Ksenia Yakovleva
Author-Email: YakovlevaKV@cbr.ru


Author-Workplace-Name: Bank of Russia, Russian Federation




Title: Text Mining-based Economic Activity Estimates

Abstract: This paper outlines the methodology for calculating a high-frequency
indicator of economic activity in Russia. News articles taken from Internet resources
are used as data sources. The news articles are analysed using text mining and
machine learning methods, which, although developed relatively recently, have
quickly found wide application in scientific research, including economic studies.
This is because news is not only a key source of information but a way to gauge the
sentiment of journalists and survey respondents about the current situation and
convert it into quantitative data.


Length: 14 pages
Creation-Date: 2017-10
Revision-Date:
Publication-Status:
File-URL: http://cbr.ru/Content/Document/File/87561/wp25_e.pdf
File-Format: Application/pdf
File-Function:
Number:wps25
Classification-JEL: C51, C81, E37.
Keywords: economic activity estimates, text mining, machine learning.
Handle:RePEc:bkr:wpaper:wps25