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Keywords Popularity Analysis Based on Hidden Markov Model

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Human Centered Computing (HCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9567))

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Abstract

This paper first analyzes the existing methods for identifying popular keywords. By studying the methods on how to define the popular keywords with their occurrence frequency, a new approach to analyze the keyword popularity will be proposed. The paper first built a new model based on the Hidden Markov Model, then introduced several parameters who impacts the feature of the model and described how the model works. Using the Stirling formula and the Viterbi algorithm to simplify the calculation. Adjusting the model’s parameters by comparing the experimental results and the output of the system. Finally, obtained a higher accuracy, effective keyword popularity analysis system.

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Acknowledgement

This work was supported by the open project of Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory (ITD-U14002 /KX142600009).

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Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Liang Xue, Zunliang Wang & Haiyang Zhang

  2. Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Beijing, China

    Liang Xue, Zunliang Wang & Wenzhi Zhang

Authors
  1. Liang Xue
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  2. Zunliang Wang
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  3. Wenzhi Zhang
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  4. Haiyang Zhang
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Corresponding author

Correspondence to Liang Xue .

Editor information

Editors and Affiliations

  1. Wuhan, Hubei, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe Ltd., Middlesex, United Kingdom

    Bo Hu

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© 2016 Springer International Publishing Switzerland

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Xue, L., Wang, Z., Zhang, W., Zhang, H. (2016). Keywords Popularity Analysis Based on Hidden Markov Model. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_43

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  • DOI: https://doi.org/10.1007/978-3-319-31854-7_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31853-0

  • Online ISBN: 978-3-319-31854-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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