Advertisement

A Situation Analysis Method for Specific Domain Based on Multi-source Data Fusion

  • Haijian Wang
  • Zhaohui Zhang
  • Pengwei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

External Internet data, as a supplement of internal data, plays an important role to decision analysis of decision makers. However, the key point of this process is to solve the problem of inconsistency between multi-source heterogeneous data. In this paper, a situation analysis method based on multi-source data fusion is proposed to analyze the situation in a specific domain. The approach consists of three main steps. Firstly, Naive Bayes multi label classification algorithm is used in the process of text topic classification and quantization to overcome the structural inconsistency of multi-source data. Secondly, a time difference correlation analysis method is used to address the time inconsistency the two time series. Finally, Support vector machine regression algorithm (SVR) is used for situation assessment in related fields. In this study, the effectiveness of the model is verified by the free-trade zone (FTZ) platform shares data, Internet news text data, and Internet statistics. The experimental results show that the method has achieved good results in the situation estimation of the related indexes.

Keywords

Multi-source data fusion Inconsistency Naive Bayes multi label classification Causal lag analysis SVR Situation estimation 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 61472004, 61602109), Shanghai Science and Technology Innovation Action Plan Project (No. 16511100903), and by The Key Laboratory of Embedded System and Service Computing of Tongji University of Ministry Education (2015).

References

  1. 1.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  2. 2.
    He, Z., Wu, J., Lv, P.: Label correlation mixture model for multi-label text categorization. In: Spoken Language Technology Workshop, pp. 83–88. IEEE, South Lake Tahoe (2015)Google Scholar
  3. 3.
    Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)CrossRefGoogle Scholar
  4. 4.
    Sehgal, V., Song, C.: SOPS: stock prediction using web sentiment. In: IEEE International Conference on Data Mining Workshops, ICDM Workshops, pp. 21–26. IEEE, Omaha (2008)Google Scholar
  5. 5.
    Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl. Based Syst. 69(1), 14–23 (2014)CrossRefGoogle Scholar
  6. 6.
    Li, J., Xu, Z., Xu, H., Tang, L., Yu, L.: Forecasting oil price trends with sentiment of online news articles. Proc. Comput. Sci. 91, 1081–1087 (2016)CrossRefGoogle Scholar
  7. 7.
    Paninski, L.: Estimation of entropy and mutual information. Neural Comput. 15(6), 1191–1253 (2014)CrossRefGoogle Scholar
  8. 8.
    Xue, L., Xiong, Y., Zhu, Y., Wu, J., Chen, Z.: Stock Trend Prediction by Classifying Aggregative Web Topic-Opinion. In: Pei, J., Tseng, Vincent S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS, vol. 7819, pp. 173–184. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37456-2_15CrossRefGoogle Scholar
  9. 9.
    Dueñas-Fernández, R., Velásquez, J.D., L’Huillier, G.: Detecting trends on the web: a multidisciplinary approach. Inf. Fusion 20, 129–135 (2014)CrossRefGoogle Scholar
  10. 10.
    Shroff, G., Agarwal, P., Dey, L.: Enterprise information fusion for real-time business intelligence. In: Proceedings of the International Conference on Information Fusion, pp. 1–8. IEEE (2011)Google Scholar
  11. 11.
    Yang, J.H., Yang, M.S.: A control chart pattern recognition system using a statistical correlation coefficient method. Comput. Ind. Eng. 48(2), 205–221 (2005)CrossRefGoogle Scholar
  12. 12.
    Guo, Z.H., Wu, J., Lu, H.Y., Wang, J.Z.: A case study on a hybrid wind speed forecasting method using BP neural network. Knowl. Based Syst. 24(7), 1048–1056 (2011)CrossRefGoogle Scholar
  13. 13.
    Bao, Y.K., Liu, Z.T., Guo, L., Wang, W.: Forecasting stock composite index by fuzzy support vector machines regression. In: International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3535–3540. IEEE (2005)Google Scholar
  14. 14.
    Mccallum, A.: A comparison of event models for Naive Bayes text classification. In: Proceedings of AAAI-1998 Workshop on Learning for Text Categorization, vol. 62, pp. 41–48 (1998)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyDong Hua UniversityShanghaiChina
  2. 2.The Key Laboratory of Embedded System and Service ComputingMinistry of Education, Tongji UniversityShanghaiChina
  3. 3.Shanghai Engineering Research Center of Network Information ServicesShanghaiChina

Personalised recommendations