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A metric to search for relevant words

Author

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  • Zhou, Hongding
  • Slater, Gary W.

Abstract

We propose a new metric to evaluate and rank the relevance of words in a text. The method uses the density fluctuations of a word to compute an index that measures its degree of clustering. Highly significant words tend to form clusters, while common words are essentially uniformly spread in a text. If a word is not rare, the metric is stable when we move any individual occurrence of this word in the text. Furthermore, we prove that the metric always increases when words are moved to form larger clusters, or when several independent documents are merged. Using the Holy Bible as an example, we show that our approach reduces the significance of common words when compared to a recently proposed statistical metric.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:329:y:2003:i:1:p:309-327
    DOI: 10.1016/S0378-4371(03)00625-3
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    Citations

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    Cited by:

    1. Rosso, Osvaldo A. & Craig, Hugh & Moscato, Pablo, 2009. "Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 916-926.
    2. Ke, Xiaohua & Zeng, Yongqiang & Ma, Qinghua & Zhu, Lin, 2014. "Complex dynamics of text analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 307-314.
    3. 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.
    4. 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.
    5. Antiqueira, L. & Nunes, M.G.V. & Oliveira Jr., O.N. & F. Costa, L. da, 2007. "Strong correlations between text quality and complex networks features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 811-820.
    6. Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.

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