Detecting Unexpected Correlation between a Current Topic and Products from Buzz Marketing Sites

  • Takako Hashimoto
  • Tetsuji Kuboyama
  • Yukari Shirota
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7108)

Abstract

This paper proposes a method to detect unexpected correlation from between a current topic and products word of mouth in buzz marketing sites, which will be part of a new approach to marketing analysis. For example, in 2009, the super-flu virus spawned significant effects on various product marketing domains around the globe. In buzz marketing sites, there had been a lot of word of mouth about the "flu." We could easily expect an "air purifier" to be correlated to the "flu" and air purifiers’ shipments had grown according to the epidemic of flu. On the other hand, the relatedness between the "flu" and a "camera" could not be easily expected. However, in Japan, consumers’ unforeseen behavior like the reluctance to buy digital cameras because of cancellations of a trip, a PE festival or other events caused by the epidemic of flu had appeared, and a strong correlation between the "flu" and "camera" had been found. Detecting these unforeseen consumers’ behavior is significant for today’s marketing analysis. In order to detect such unexpected relations, this paper applies the dynamic time warping techniques. Our proposed method computes time series correlations between a current topic and unspecified products from word of mouth of buzz marketing sites, and finds product candidates which have unexpected correlation with a current topic. To evaluate the effectiveness of the method, the experimental results for the current topic ("flu") and products ("air purifier", "camera", "car", etc.) are shown as well. By detecting unexpected relatedness from buzz marketing sites, unforeseen consumer behaviors can be further analyzed.

Keywords

Data mining Marketing analysis Web Intelligence Dynamic time warping Social media analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shimoji, Y., Wada, T., Hirokawa, S.: Dynamic Thesaurus Construction from English-Japanese Dictionary. In: The Second International Conference on Complex, Intelligent and Software Intensive Systems, pp. 918–923 (2008)Google Scholar
  2. 2.
    kakaku.com, http://kakaku.com/
  3. 3.
    Nagano, S., Inaba, M., Mizoguchi, Y., Iida, T., Kawamura, T.: Ontology-Based Topic Extraction Service from Weblogs. In: IEEE International Conference on Semantic Computing, pp. 468–475 (2008)Google Scholar
  4. 4.
    Kobayshi, N., Inui, K., Matusmoto, Y., Tateishi, K., Fukushima, S.: Collecting evaluative expressions by a text mining technique. IPSJ SIG NOTE 154(12), 77–84 (2003)Google Scholar
  5. 5.
    Asano, H., Hirano, T., Kobayasi, N., Matsuno, Y.: Subjective Information Indexing Technology Analyzing Word-of-mouth Content on the Web. NTT Technical Review 6(9), 1–7 (2008)Google Scholar
  6. 6.
    Spangler, W.S., Chen, Y., Proctor, L., Lelescu, A., Behal, A., He, B., Griffin, T.D., Liu, A., Wade, B., Davis, T.: COBRA - mining web for COrporate Brand and Reputation Analysis. Web Intelligence and Agent Systems (WIAS) 7(3), 243–254 (2009)Google Scholar
  7. 7.
    Zhu, Y., Shasha, D.: Warping Indexes with Envelope Transforms for Query by Humming. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 181–192 (2003)Google Scholar
  8. 8.
    Otranto, E.: Identifying financial time series with similar dynamic conditional correlation. Journal of Computational Statistics & Data Analysis archive 54(1), 1–15 (2010)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Loy, C., Xiang, T., Gong, S.: International Journal of Computer Vision 90(1), 106–129 (2010)CrossRefGoogle Scholar
  10. 10.
    Radinsky, K., Agichtein, E., Gabrilovich, E., Markovitch, S.: A Word at a Time: Computing Word Relatedness using Tremporal Semantic Analysis. In: WWW 2011, pp. 337–346 (2011)Google Scholar
  11. 11.
    Wang, G., Araki, K.: A Graphic Reputation Analysis System for Mining Japanese Weblog Based on both Unstructured and Structured Information. In: AINA Workshops 2008, pp. 1240–1245 (2008)Google Scholar
  12. 12.
    Iino, Y., Hirokawa, S.: Time Series Analysis of R&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
  13. 13.
    Hashimoto, T., Shirota, Y.: Semantics Extraction from Social Computing: A Framework of Reputation Analysis on Buzz Marketing Sites. In: Kikuchi, S., Sachdeva, S., Bhalla, S. (eds.) DNIS 2010. LNCS, vol. 5999, pp. 244–255. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters 18(8), 689–694 (1997)CrossRefGoogle Scholar
  15. 15.
    Hashimoto, T., Kuboyama, T., Shirota, Y.: Graph-based Consumer Behavior Analysis from Buzz Marketing Sites. In: Proc. of 21st European Japanese Conference on Information Modelling and Knowledge Bases, pp. 60–71 (2011)Google Scholar
  16. 16.
    Kuboyama, T., Hashimoto, T., Shirota, Y.: Consumer Behavior Analysis from Buzz Marketing Sites over Time Series Concept Graphs. In: Proc. of 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, pp. 73–83 (2011)Google Scholar
  17. 17.
    Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proc. of Advances in Knowledge Discovery and Data Mining, pp. 229–248. AAAI/MIT (1994)Google Scholar
  18. 18.
    R, a language and environment for statistical computing and graphics, http://www.r-project.org/
  19. 19.
    Zhu, Y., Shasha, D.: Efficient Elastic Burst Detection in Data Streams. In: Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 336–345 (2003)Google Scholar
  20. 20.
    Kleinberg, K.: Bursty and hierarchical structure in streams. In: Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 91–101 (2002)Google Scholar
  21. 21.
    Salvadore, S., Chan, P.: FastDTW: Toward accurate dynamic time warping in linear time and space. In: Proc. of 3rd Workshop on Mining Temporal and Sequential Data, pp. 561–580 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Takako Hashimoto
    • 1
  • Tetsuji Kuboyama
    • 2
  • Yukari Shirota
    • 3
  1. 1.Commerce and EconomicsChiba University of CommerceChibaJapan
  2. 2.Computer CenterGakushuin UniversityTokyoJapan
  3. 3.Department of Management, Faculty of EconomicsGakushuin UniversityTokyoJapan

Personalised recommendations