Opinion Discrimination Using Complex Network Features

  • Diego R. Amancio
  • Renato Fabbri
  • Osvaldo N. OliveiraJr.
  • Maria G. V. Nunes
  • Luciano da Fontoura Costa
Part of the Communications in Computer and Information Science book series (CCIS, volume 116)


Topological and dynamic features of complex networks have proven in recent years to be suitable for capturing text characteristics, with various applications in natural language processing. In this article we show that texts with positive and negative opinions can be distinguished from each other when represented as complex networks. The distinction was possible with the use of several metrics, including degrees, clustering coefficient, shortest paths, global efficiency, closeness and accessibility. The multidimensional dataset was projected into a 2-dimensional space with the principal component analysis. The distinction was quantified using machine learning algorithms, which allowed a recall of 84.4% in the automatic discrimination for the negative opinions, even without attempts to optimize the pattern recognition process.


Complex Network Linear Discriminant Analysis Natural Language Processing Machine Translation Positive Opinion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Manning, C.D., Schuetze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)Google Scholar
  2. 2.
    Antiqueira, L., Nunes, M.G.V., Oliveira Jr., O.N., Costa, L.F.: Strong correlations between text quality and complex networks features. Physica A 373, 811–820 (2007)CrossRefGoogle Scholar
  3. 3.
    Newman, M.E.J.: The Structure and Function of Complex Networks. SIAM Review 45, 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Albert, R.Z., Barabasi, A.L.: Statistical Mechanics of Complex Networks. Rev. Modern Phys. 74, 47–97 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Ferrer i Cancho, R., Sole, R.V.: The small world of human language. Proceedings of the Royal Society of London B 268, 2261 (2001)CrossRefGoogle Scholar
  6. 6.
    Barabasi, A.L.: Scale-Free Networks: a decade and beyond. Science 24, 412–413 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Antiqueira, L., Oliveira Jr., O.N., Costa, L.F., Nunes, M.G.V.: A Complex Network Approach to Text Summarization. Information Sciences 179(5), 584–599 (2009)CrossRefzbMATHGoogle Scholar
  8. 8.
    Amancio, D.R., Antiqueira, L., Pardo, T.A.S., Costa, L.F., Oliveira Jr., O.N., Nunes, M.G.V.: Complex networks analysis of manual and machine translations. International Journal of Modern Physics C 19(4), 583–598 (2008)CrossRefzbMATHGoogle Scholar
  9. 9.
    Amancio, D.R., Nunes, M.G.V., Oliveira Jr., O.N., Pardo, T.A.S., Antiqueira, L., da Costa, L.F.: Using metrics from complex networks to evaluate machine translation. Physica A 390(1), 131–142 (2011)CrossRefGoogle Scholar
  10. 10.
    Sigman, M., Cecchi, G.A.: Global Organization of the Wordnet Lexicon. Proceedings of the National Academy of Sciences 99, 1742–1747 (2002)CrossRefGoogle Scholar
  11. 11.
    Costa, L.F.: What’s in a name? International Journal of Modern Physics C 15, 371–379 (2004)CrossRefGoogle Scholar
  12. 12.
    Dorogovtsev, S.V., Mendes, J.F.F.: Evolution of networks. Advances in Physics 51, 1079–1187 (2002)CrossRefGoogle Scholar
  13. 13.
    Antiqueira, L., Pardo, T.A.S., Nunes, M.G.V., Oliveira Jr., O.N., Costa, L.F. Some issues on complex networks for author characterization. In: Proceeedings of the Workshop in Information and Human Language Technology (2006)Google Scholar
  14. 14.
    Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Systems with Applications 36(7), 10760–10773 (2009)CrossRefGoogle Scholar
  15. 15.
    Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological aspects of natural language. use: our words, our selves. Annual Review of Psychology 54, 547–577 (2003)CrossRefGoogle Scholar
  16. 16.
    Costa, L.F., et al.: Characterization of complex networks: a survey of measurements. Advances in Physics 56, 167–242 (2007)CrossRefGoogle Scholar
  17. 17.
    Rodrigues, F.A., Costa, L. F.: A structure dynamic approach to cortical organization: Number of paths and accessibility. Journal of Neuroscience Methods, 1–10 (2009)Google Scholar
  18. 18.
    Ratnaparki, A.: A Maximum Entropy Part-Of-Speech Tagger. In: Proceedings of the Empirical Methods in Natural Language Processing Conference (1996)Google Scholar
  19. 19.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  20. 20.
    Jolliffe, I.T.: Principal component analysis. Springer, New York (2002)zbMATHGoogle Scholar
  21. 21.
    Costa, L.F., Cesar Jr., R.M.: Shape Analysis and Classification. CRC Press, Boca Raton (2001)zbMATHGoogle Scholar
  22. 22.
    McLachlan, G.J.: Discriminant Analysis and Statistical Pattern Recognition. Wiley, Chichester (2004)zbMATHGoogle Scholar
  23. 23.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons Inc., Chichester (2001)zbMATHGoogle Scholar
  24. 24.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar
  25. 25.
    Cohen, W.W.: Fast Effective Rule Induction. In: 12 International Converence on Machine Learning, pp. 115–223 (1995)Google Scholar
  26. 26.
    John, G.H., Langley, P.: Estimating Continuous Distribution in Bayesian Classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)Google Scholar
  27. 27.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 12, pp. 1137–1143 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Diego R. Amancio
    • 1
  • Renato Fabbri
    • 1
  • Osvaldo N. OliveiraJr.
    • 1
  • Maria G. V. Nunes
    • 1
  • Luciano da Fontoura Costa
    • 1
  1. 1.University of São PauloSão CarlosBrazil

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