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2020 | OriginalPaper | Chapter

4. Text Mining Support to Pandemic Planning

Authors : Desheng Dash Wu, David L. Olson

Published in: Pandemic Risk Management in Operations and Finance

Publisher: Springer International Publishing

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Abstract

Text mining is a useful tool to identify sentiment. Not only it is widely used in stock market operations but it can also be applied to analyze Web content or other documents related to pandemic operations. A support vector machine is a data mining algorithm useful for certain types of data. This chapter demonstrates the use of a Web crawler to identify financial sentiment, a process that would also work for pandemic management. A support vector model is applied to a Chinese stock market index, demonstrating that technology also could be extended to pandemic planning and control.

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previous chapter System Dynamics Modeling of Contagion Effects
next chapter Macroeconomic Impact
Literature
1.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C. (eds) Mining Text Data. (springer, Heidelberg, pp. 415-463).
2.
Kelly, F. C. (2003). Why you win or lose: The psychology of speculation. Courier Corporation.
3.
Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers and Operations Research, 32(10), 2513–2522. CrossRef
4.
Yu, L., Chen, H., Wang, S., & Lai, K. K. (2009). Evolving least squares support vector machines for stock market trend mining. IEEE Transactions on Evolutionary Computation, 13(1), 87–102. CrossRef
5.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. CrossRef
Metadata
Title
Text Mining Support to Pandemic Planning
Authors
Desheng Dash Wu
David L. Olson
Copyright Year
2020
Publisher
Springer International Publishing
Book
Pandemic Risk Management in Operations and Finance

Print ISBN: 978-3-030-52196-7

Electronic ISBN: 978-3-030-52197-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-52197-4_4