Automatic Extraction of Basis Expressions That Indicate Economic Trends

  • Hiroki Sakaji
  • Hiroyuki Sakai
  • Shigeru Masuyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)

Abstract

This paper proposes a method to automatically extract basis expressions that indicate economic trends from newspaper articles by using a statistical method. We also propose a method to classify them into positive expressions that indicate upbeat, and negative expressions that indicate downturn in economy, respectively. It is important for companies, governments and investors to predict economic trends in order to forecast revenue, sales of products, prices of commodities and stock prices. We considered that basis expressions are useful for the companies, governments and investors to forecast economic trends. We extracted basis expressions, and classified them into positive expressions or negative expressions as information to forecast economic trends. Our method used a bootstrap method that was minimally a supervised algorithm for extracting basis expressions. Moreover, our method classified basis expressions into positive expressions or negative ones without dictionaries.

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References

  1. 1.
    Nakajima, T., Sakai, H., Masuyama, S.: A classification method based on the view of the author of each newspaper article on economics. IPSJ SIG Notes 2003(51)(20030522), 175–180 (2003) (in Japanese)Google Scholar
  2. 2.
    Sakai, H., Umemura, S., Masuyama, S.: Extraction of Expressions concerning Accident Cause contained in Articles on Traffic Accidents. Journal of Natural Language Processing 13(4), 99–124 (2006) (in Japanese)Google Scholar
  3. 3.
    Sakai, H., Masuyama, S.: Extraction of Cause Information from Newspaper Articles Concerning Business Performance. In: Proc. of the 4th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI 2007), pp. 205–212 (2007)Google Scholar
  4. 4.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1999)Google Scholar
  5. 5.
    Kanayama, H., Nasukawa, T., Watanabe, H.: Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th COLING, pp. 494–500 (2004)Google Scholar
  6. 6.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of HLT/EMNLP-2005, pp. 347–354 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hiroki Sakaji
    • 1
  • Hiroyuki Sakai
    • 1
  • Shigeru Masuyama
    • 1
  1. 1.Toyohashi University of TechnologyToyohashi-shiJapan

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