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Text mining by Tsallis entropy

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  • Jamaati, Maryam
  • Mehri, Ali

Abstract

Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms’ relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.

Suggested Citation

  • Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1368-1376
    DOI: 10.1016/j.physa.2017.09.020
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    References listed on IDEAS

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    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. 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.
    3. Ali Mehri & Sahar Mohammadpour Lashkari, 2016. "Power-law regularities in human language," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(11), pages 1-6, November.
    4. J. P. Herrera & P. A. Pury, 2008. "Statistical keyword detection in literary corpora," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(1), pages 135-146, May.
    5. Zhou, Hongding & Slater, Gary W., 2003. "A metric to search for relevant words," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 309-327.
    6. Yang, Zhen & Lei, Jianjun & Fan, Kefeng & Lai, Yingxu, 2013. "Keyword extraction by entropy difference between the intrinsic and extrinsic mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4523-4531.
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    Cited by:

    1. Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Liu, Yanyan & Li, Keping & Yan, Dongyang & Gu, Shuang, 2022. "A network-based CNN model to identify the hidden information in text data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    3. Ramezani, Zahra & Pourdarvish, Ahmad, 2021. "Transfer learning using Tsallis entropy: An application to Gravity Spy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(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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