Node-First Causal Network Extraction for Trend Analysis Based on Web Mining

  • Hideki Kawai
  • Katsumi Tanaka
  • Kazuo Kunieda
  • Keiji Yamada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6882)


In this paper, we propose a node-first causal extraction method for trend analysis. Recently, it has become more important for business leaders, politicians and academics to understand broader and longer environmental trends because of the need to develop better strategies for dealing with current and future issues. Trend analysis is often utilized to identify key factors in political, economical, social and technological trends. We propose a web mining framework that can extract a causal network of key factors underlying macro trends related to a user’s interest. The main idea can be described as ”node-first” approach, which recursively identifies key factors relevant to a user’s query, then verifies causal relations between key factors. As the result of experiment, we demonstrate high precision of key factor identification (P@100 = 0.76) and causality verifications (F-value = 0.74).


Causal Relation Trend Analysis Causal Network Causal Loop Diagram Causal Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hideki Kawai
    • 1
  • Katsumi Tanaka
    • 2
  • Kazuo Kunieda
    • 1
  • Keiji Yamada
    • 1
  1. 1.NEC C & C Innovation Research LaboratoriesIkoma cityJapan
  2. 2.Graduate School of InfomaticsKyoto UniversityKyotoJapan

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