Search and Analysis of Bankruptcy Cause by Classification Network

  • Sachio Hirokawa
  • Takahiro Baba
  • Tetsuya Nakatoh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6918)


A simple document search is insufficient when we analyse corporate information. Not only a list of search results, but also a justification why the results match the query condition is important. This paper proposes a method to extract cause of bankruptcy from news articles applying the co-occurrence analysis of words.


Formal Concept Analysis Correct Word Feature Word Concept Graph Bankruptcy Prediction 
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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  1. 1.
    Baba, T., Liu, L., Hirokawa, S.: Formal Concept Analysis of Medical Incident Reports. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6278, pp. 207–214. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Aoshima, T., Fukuta, N., Yokoyama, S., Ishikawa, H.: A Proposal of Constrained Clustering of Micro-Blogs. In: DEIM 2010 B1-3 (2010) (in Japanese)Google Scholar
  3. 3.
    Carpineto, C., Romano, G.: Concept Data Analysis Theory and Application. John Wiley and Sons, Chichester (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Egoshi, R., Nagai, H., Nakamura, T.: Extraction of Important Articles from Related Articles using Small World Structure. IPSJ SIG. Notes (113), 17–22 (2008) (in Japanese)Google Scholar
  5. 5.
    Ganter, G., Wille, R., Franzke, C.: Formal Concept Analysis Mathematical Foundation. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Izumi, K., Goto, T., Matsui, T.: Analysis of Financial Markets Fluctuation by Textual Information. Journal of JSAI 25(3), 383–387 (2010) (in Japanese)Google Scholar
  7. 7.
    Kishida, K.: Property of Mean Average Precision as Performance Measure in Retrieval Experiment. IPSJ SIG. Notes (74), 97–104 (2001) (in Japanese)Google Scholar
  8. 8.
    Shirata, C.Y., Takeuchi, H., Ogino, S., Watanabe, H.: Financial Analysis using Text Mining Technique: Empirical Analysis of Bankrupt Companies. Business Analysis Association Annual Report (25), 40–47 (2009) (in Japanese)Google Scholar
  9. 9.
    Chan, S.W.K.: Extraction of salient textual patterns: Synergy between lexical cohesion and contextual coherence. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 34(2), 205–218 (2004)CrossRefGoogle Scholar
  10. 10.
    Chuang, W.T., Yang, J.: Extracting sentence segments for text summarization: A machine learning approach. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 152–159 (2000)Google Scholar
  11. 11.
    Goda, S., Ohsawa, Y.: Chance discovery in credit risk management -Time order method and directed KeyGraph for estimation of chain reaction bankruptcy structure. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 247–254. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Huff, A.S. (ed.): Mapping Strategic Thought. Wiley, Chichester (1990)Google Scholar
  13. 13.
    Iino, Y., Hirokawa, S.: Time Series Analysis of R and D Team Using Patent Information. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009. LNCS, vol. 5712, pp. 464–471. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Kida, M.: Cognitive research of Asahi ’s organizational renewal -textminig of annual reports organizational science. Academic Journal 39(4) (2006)Google Scholar
  15. 15.
    Koester, B.: Conceptual Knowledge Retrieval with FooCA: Improving Web Search Engine Results with Contexts anc Concept Hierarchies. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 176–190. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Li, B., Zhou, L., Fen, S., Wong, K.-F.: A Unified Graph Model for Sentence-based Opinion Retrieval. In: Proc. 48th ACL, pp. 1367–1375 (2010)Google Scholar
  17. 17.
    Li, H., Sun, J., Wu, J.: Predicting business failure using classification and regression tree: An empirical comparison with popular classifical statistical methods and top classification mining methods. Expert Systems with Applications 37(8), 5895–5904 (2010)CrossRefGoogle Scholar
  18. 18.
    Liu, X., Webster, J., Kit, C.: An extractive text summarizer based on significant words. In: Li, W., Mollá-Aliod, D. (eds.) ICCPOL 2009. LNCS (LNAI), vol. 5459, pp. 168–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Ouyang, Y., Li, W., Wei, F., Lu, Q.: Learning similar ity functions in graph-based document summarization. In: Li, W., Mollá-Aliod, D. (eds.) ICCPOL 2009. LNCS (LNAI), vol. 5459, pp. 189–200. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Mining System based on Search Engine and Concept Graph for Large-Scale Financial Report Texts. In: Proc. 2nd IEEE ICIFE, pp. 675–679 (2010)Google Scholar
  21. 21.
    Shimoji, Y., Wada, T., Hirokawa, S.: Dynamic Thesaurus Construction from English- Japanese Dictionary. In: Proc. The Second International Conference on Complex, Intelligent and Software Intensive Systems, pp. 918–923 (2008)Google Scholar
  22. 22.
    Shin, K.-S., Lee, T.-S., Kim, H.-J.: An application of support vector machines in bankruptcy prediction model. Expert Systems with Application 28(1), 127–135 (2005)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Yamamoto, Y., Orihara, R.: Keyword Extraction using the Word Co-occurrence Network Properties that is Independent of Languages and Document Types and Its Evaluation by Prediction of Headline Words. Trans. JSAI 24(3), 303–312 (2009) (in Japanese)Google Scholar
  25. 25.
    Takeuchi, H., Ogino, S., Watanabe, H., Shirata, Y.: Context-based text mining for insights in long documents. In: Yamaguchi, T. (ed.) PAKM 2008. LNCS (LNAI), vol. 5345, pp. 123–134. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Tsai, C.-F.: Feature selection in bankruptcy prediction. Knowledge-Based Systems 22, 120–127 (2009)CrossRefGoogle Scholar
  27. 27.
    Turney, P.D.: Learning algorithms for keyphrase extraction. Information Retrieval 2(4), 303–336 (2000)CrossRefGoogle Scholar
  28. 28.
    Uchida, Y., Yoshikawa, T., Furuhashi, T., Hirao, E., Iguchi, H.: Extraction of important keywords in free text of questionnaire data and visualization of relationship among sentences. In: IEEE International Conference on Fuzzy Systems, art. no. 5277332, pp. 1604–1608 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sachio Hirokawa
    • 1
  • Takahiro Baba
    • 2
  • Tetsuya Nakatoh
    • 1
  1. 1.Research Institute for Information TechnologyKyushu UniversityJapan
  2. 2.Graduate School of Information Science and Electrical EngineeringKyushu UniversityJapan

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