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The role of entropy in word ranking

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  • Mehri, Ali
  • Darooneh, Amir H.

Abstract

Entropy as a measure of complexity in the systems has been applied for ranking the words in the human written texts. We introduce a novel approach to evaluate accuracy for retrieved indices. We also have an illustrative comparison between proposed entropic metrics and some other methods in extracting the keywords. It seems that, some of the discussed metrics apply similar features for word ranking in the text. This work recommend the entropy as a systematic measure in text mining.

Suggested Citation

  • Mehri, Ali & Darooneh, Amir H., 2011. "The role of entropy in word ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(18), pages 3157-3163.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:18:p:3157-3163
    DOI: 10.1016/j.physa.2011.04.013
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    References listed on IDEAS

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    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, November.
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    Cited by:

    1. Carretero-Campos, C. & Bernaola-Galván, P. & Coronado, A.V. & Carpena, P., 2013. "Improving statistical keyword detection in short texts: Entropic and clustering approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1481-1492.
    2. Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.
    3. Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Mehri, Ali & Agahi, Hamzeh & Mehri-Dehnavi, Hossein, 2019. "A novel word ranking method based on distorted entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 484-492.

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