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Webpage Mining for Inflation Emergency Early Warning

  • Yan Qu
  • Wei Shang
  • Shouyang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)

Abstract

Serious inflation turbulence is a signal of potential financial crisis and social economic emergencies. Macroeconomic early warning of inflation and other major economic indicators is critical to discover potential crisis in advance. Traditional early warning methods are based on official economic statistics which are usually either released at least one month later than the economic activities actually occur or lack of flexibility to cope with fast business changes. With proper extraction and aggregation, huge amount of Internet data can serve as a new complementarity source to facilitate more timely and accurate emergency early warning. This research innovatively adopts web data processing techniques and text mining methods to extract useful information from huge amount of Internet news reports. Based on the extracted information, a price sentiment index is proposed to detect turning points of inflation efficiently. Empirical evaluation proved that the price sentiment index is efficient in inflation emergency early warning.

Keywords

sentiment analysis web mining macroeconomic early warning Consumer Price Index 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yan Qu
    • 1
  • Wei Shang
    • 1
  • Shouyang Wang
    • 1
  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of ScienceBeijingChina

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