Comparison Analysis for Text Data by Integrating Two FACT-Graphs

  • Ryosuke Saga
  • Hiroshi Tsuji
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)


This paper describes a method to visualize contrast information about two targets the Frequency and Co-occurrence Trend (FACT)-Graph. FACT-Graph is a method to visualize the changes in keyword trends and relationships between terms over two time periods. We have used FACT-Graphs as comparison method between two targets in previous research; however, the method cannot compare them as equals. To visualize contrast information, we combine two FACT-Graphs generated from different viewpoints and express the features in one graph. In case study by using 132 articles from two newspapers, we compare topics such as politics and events in them.


Contrast Mining Visualization FACT-Graph Text Mining Knowledge Management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tiwana, A.: The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms. Prentice Hall (2002)Google Scholar
  2. 2.
    Inmon, W.H.: Building the Data Warehouse. John Wiley & Sons, Inc. (2005)Google Scholar
  3. 3.
    Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press (2007)Google Scholar
  4. 4.
    Saga, R., Terachi, M., Sheng, Z., Tsuji, H.: FACT-Graph: Trend Visualization by Frequency and Co-occurrence. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 308–315. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Saga, R., Tsuji, H., Tabata, K.: Loopo: Integrated Text Miner for FACT-Graph-Based Trend Analysis. In: Salvendy, G., Smith, M.J. (eds.) HCI I 2009. Part II. LNCS, vol. 5618, pp. 192–200. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Saga, R., Tsuji, H., Miyamoto, T., Tabata, K.: Development and case study of trend analysis software based on FACT-Graph. Artificial Life and Robotics 15, 234–238 (2010)CrossRefGoogle Scholar
  7. 7.
    Saga, R., Miyamoto, T., Tsuji, H., Matsumoto, K.: FACT-Graph in Web Log Data. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS, vol. 6884, pp. 271–279. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Terachi, M., Saga, R., Tsuji, H.: Trends Recognition. In: IEEE International Conference on Systems, Man & Cybernetics (IEEE/SMC 2006), pp. 4784–4789 (2006)Google Scholar
  9. 9.
    Saga, R., Takamizawa, S., Kitami, K., Tsuji, H., Matsumoto, K.: Comparison Analysis for Text Data by Using FACT-Graph. In: Salvendy, G., Smith, M.J. (eds.) HCII 2011, Part II. LNCS, vol. 6772, pp. 75–83. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Saga, R., Takamizawa, S., Tsuji, H., Matsumoto, K.: Comparison Analysis for Editorials by Reversible FACT-Graph. In: Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2011), pp. 216–221 (2011)Google Scholar
  11. 11.
    Baayen, R.H.: Word Frequency Distributions. Springer (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka Prefecture UniversitySakaiJapan

Personalised recommendations