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Strong correlations between text quality and complex networks features

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  • Antiqueira, L.
  • Nunes, M.G.V.
  • Oliveira Jr., O.N.
  • F. Costa, L. da

Abstract

Concepts of complex networks have been used to obtain metrics that were correlated to text quality established by scores assigned by human judges. Texts produced by high-school students in Portuguese were represented as scale-free networks (word adjacency model), from which typical network features such as the in/outdegree, clustering coefficient and shortest path were obtained. Another metric was derived from the dynamics of the network growth, based on the variation of the number of connected components. The scores assigned by the human judges according to three text quality criteria (coherence and cohesion, adherence to standard writing conventions and theme adequacy/development) were correlated with the network measurements. Text quality for all three criteria was found to decrease with increasing average values of outdegrees, clustering coefficient and deviation from the dynamics of network growth. Among the criteria employed, cohesion and coherence showed the strongest correlation, which probably indicates that the network measurements are able to capture how the text is developed in terms of the concepts represented by the nodes in the networks. Though based on a particular set of texts and specific language, the results presented here point to potential applications in other instances of text analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:373:y:2007:i:c:p:811-820
    DOI: 10.1016/j.physa.2006.06.002
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    2. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    3. Theo Frottier & Bertrand Georgeot & Olivier Giraud, 2022. "Harmonic structures of Beethoven quartets: a complex network approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(7), pages 1-8, July.
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    5. Amancio, Diego R. & Oliveira Jr., Osvaldo N. & Costa, Luciano da F., 2012. "Structure–semantics interplay in complex networks and its effects on the predictability of similarity in texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4406-4419.
    6. 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.
    7. Cui, Xue-Mei & Yoon, Chang No & Youn, Hyejin & Lee, Sang Hoon & Jung, Jean S. & Han, Seung Kee, 2017. "Dynamic burstiness of word-occurrence and network modularity in textbook systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 103-110.
    8. D. R. Amancio & M. G. V. Nunes & O. N. Oliveira & L. F. Costa, 2012. "Using complex networks concepts to assess approaches for citations in scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 827-842, June.
    9. Amancio, D.R. & Nunes, M.G.V. & Oliveira, O.N. & Pardo, T.A.S. & Antiqueira, L. & da F. Costa, L., 2011. "Using metrics from complex networks to evaluate machine translation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 131-142.
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    11. Ausloos, M., 2012. "Measuring complexity with multifractals in texts. Translation effects," Chaos, Solitons & Fractals, Elsevier, vol. 45(11), pages 1349-1357.

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