A Review on Bayesian Networks for Sentiment Analysis

  • Luis Gutiérrez
  • Juan Bekios-Calfa
  • Brian Keith
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)


This article presents a review of the literature on the application of Bayesian networks in the field of sentiment analysis. This is done in the context of a research project on text representation and use of Bayesian networks for the determination of emotions in the text. We have analyzed relevant articles that correspond mainly to two types, some in which Bayesian networks are used directly as classification methods and others in which they are used as a support tool for classification, by extracting features and relationships between variables. Finally, this review presents the bases for later works that seek to develop techniques for representing texts that use Bayesian networks or that, through an assembly scheme, allow for superior classification performance.


Bayesian networks Sentiment analysis Literature review Opinion mining 



Research partially funded by the National Commission of Scientific and Technological Research (CONICYT) and the Ministry of Education of the Government of Chile. Project REDI170607: “Multidimensional Bayesian classifiers for the interpretation of text and video emotions”.


  1. 1.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: ICWSM, vol. 10, no. 1, pp. 178–185 (2010)Google Scholar
  2. 2.
    Li, Y.M., Li, T.Y.: Deriving market intelligence from microblogs. Decis. Support Syst. 55(1), 206–217 (2013)CrossRefGoogle Scholar
  3. 3.
    Ren, F., Quan, C.: Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing. Inf. Technol. Manag. 13(4), 321–332 (2012)CrossRefGoogle Scholar
  4. 4.
    Nagamma, P., Pruthvi, H., Nisha, K., Shwetha, N.: An improved sentiment analysis of online movie reviews based on clustering for box-office prediction. In: 2015 International Conference on Computing, Communication and Automation (ICCCA), pp. 933–937. IEEE (2015)Google Scholar
  5. 5.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer Science & Business Media (2011)Google Scholar
  6. 6.
    Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl. Based Syst. 89, 14–46 (2015)CrossRefGoogle Scholar
  7. 7.
    Akter, S., Wamba, S.F.: Big data analytics in e-commerce: a systematic review and agenda for future research. Electron. Mark. 26(2), 173–194 (2016)CrossRefGoogle Scholar
  8. 8.
    Alaei, A.R., Becken, S., Stantic, B.: Sentiment analysis in tourism: capitalizing on big data. J. Travel. Res. (2017).
  9. 9.
    Sehgal, D., Agarwal, A.K.: Real-time sentiment analysis of big data applications using Twitter data with Hadoop framework. In: Soft Computing: Theories and Applications, pp. 765–772. Springer (2018)Google Scholar
  10. 10.
    Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A.: A practical guide to sentiment analysis, vol. 5. Springer (2017)Google Scholar
  11. 11.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167 (2012)Google Scholar
  12. 12.
    Grosan, C., Abraham, A.: Intelligent systems. Springer (2011)Google Scholar
  13. 13.
    Mononen, T., Myllymӓki, P.: Fast NML computation for Naive Bayes models. In: International Conference on Discovery Science, pp. 151–160. Springer (2007)Google Scholar
  14. 14.
    Kass, R.E., Raftery, A.E.: Bayes factors. J. Am. Stat. Assoc. 90(430), 773–795 (1995)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jensen, F.V.: An Introduction to Bayesian Networks, vol. 210. UCL Press, London (1996)Google Scholar
  16. 16.
    Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20(3), 197–243 (1995)zbMATHGoogle Scholar
  17. 17.
    Bernardo, J.M., Smith, A.F.: Bayesian Theory (2001)Google Scholar
  18. 18.
    Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)zbMATHGoogle Scholar
  19. 19.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
  20. 20.
    Driver, E., Morrell, D.: Implementation of continuous Bayesian networks using sums of weighted Gaussians. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 134–140. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
  21. 21.
    Friedman, N., Goldszmidt, M., et al.: Discretizing continuous attributes while learning Bayesian networks. In: ICML, pp. 157–165 (1996)Google Scholar
  22. 22.
    Ding, J.: Probabilistic inferences in Bayesian networks. arXiv preprint arXiv:1011.0935 (2010)
  23. 23.
    Wellman, M.P., Henrion, M.: Explaining ‘explaining away’. IEEE Trans. Pattern Anal. Mach. Intell. 15(3), 287–292 (1993)CrossRefGoogle Scholar
  24. 24.
    Zhi-Qiang, L.: Causation, Bayesian networks, and cognitive maps. Acta Autom. Sin. 27(4), 552–566 (2001)MathSciNetGoogle Scholar
  25. 25.
    Milho, I., Fred, A., Albano, J., Baptista, N., Sena, P.: An auxiliary system for medical diagnosis based on Bayesian belief networks. In: Proceedings of 11th Portuguese Conference on Pattern Recognition, RECPAD (2000)Google Scholar
  26. 26.
    Mori, J., Mahalec, V.: Inference in hybrid Bayesian networks with large discrete and continuous domains. Expert Syst. Appl. 49, 1–19 (2016)CrossRefGoogle Scholar
  27. 27.
    Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42(2–3), 393–405 (1990)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Heckerman, D.: A tutorial on learning with Bayesian networks. In: Learning in Graphical Models, pp. 301–354. Springer (1998)Google Scholar
  29. 29.
    Orimaye, S.O., Pang, Z.Y., Setiawan, A.M.P.: Learning sentiment dependent Bayesian Network classifier for online product reviews. Informatica 40(2), 225 (2016)Google Scholar
  30. 30.
    Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 101–108. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  31. 31.
    Airoldi, E., Bai, X., Padman, R.: Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts. In: International Workshop on Knowledge Discovery on the Web, pp. 167–187. Springer (2004)Google Scholar
  32. 32.
    Bai, X.: Predicting consumer sentiments from online text. Decis. Support Syst. 50(4), 732–742 (2011)CrossRefGoogle Scholar
  33. 33.
    Ortigosa-Hernández, J., Rodríguez, J.D., Alzate, L., Lucania, M., Inza, I., Lozano, J.A.: Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92, 98–115 (2012)CrossRefGoogle Scholar
  34. 34.
    Lane, P.C., Clarke, D., Hender, P.: On developing robust models for favourability analysis: model choice, feature sets and imbalanced data. Decis. Support Syst. 53(4), 712–718 (2012)CrossRefGoogle Scholar
  35. 35.
    Orimaye, S.O.: Sentiment augmented Bayesian network. In: Data Mining and Analytics 2013 (AusDM 2013), p. 89 (2013)Google Scholar
  36. 36.
    Ren, F., Kang, X.: Employing hierarchical Bayesian networks in simple and complex emotion topic analysis. Comput. Speech Lang. 27(4), 943–968 (2013)CrossRefGoogle Scholar
  37. 37.
    Wan, Y., Gao, Q.: An ensemble sentiment classification system of Twitter data for airline services analysis. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1318–1325. IEEE (2015)Google Scholar
  38. 38.
    Orimaye, S.O., Pang, Z.Y., Setiawan, A.M.P.: Towards a sentiment dependent Bayesian network classifier for online product reviews (2016)Google Scholar
  39. 39.
    Wang, L., Ren, F., Miao, D.: Multi-label emotion recognition of weblog sentence based on Bayesian networks. IEEJ Trans. Electr. Electron. Eng. 11(2), 178–184 (2016)CrossRefGoogle Scholar
  40. 40.
    Chaturvedi, I., Cambria, E., Poria, S., Bajpai, R.: Bayesian deep convolution belief networks for subjectivity detection. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 916–923. IEEE (2016)Google Scholar
  41. 41.
    Chaturvedi, I., Ong, Y.S., Tsang, I.W., Welsch, R.E., Cambria, E.: Learning word dependencies in text by means of a deep recurrent belief network. Knowl. Based Syst. 108, 144–154 (2016)CrossRefGoogle Scholar
  42. 42.
    Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y., Qawasmeh, O.: Enhancing aspect-based sentiment analysis of Arabic hotels’ reviews using morphological, syntactic and semantic features. Inf. Process. Manag. (2018).
  43. 43.
    Chen, W., Zong, L., Huang, W., Ou, G., Wang, Y., Yang, D.: An empirical study of massively parallel Bayesian networks learning for sentiment extraction from unstructured text. In: Asia-Pacific Web Conference, pp. 424–435. Springer (2011)Google Scholar
  44. 44.
    Van Der Gaag, L.C., De Waal, P.R.: Multi-dimensional Bayesian network classifiers (2006)Google Scholar
  45. 45.
    Bielza, C., Li, G., Larranaga, P.: Multi-dimensional classification with Bayesian networks. Int. J. Approx. Reason. 52(6), 705–727 (2011)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Glover, F., Laguna, M.: Tabu search. In: Handbook of Combinatorial Optimization, pp. 3261–3362. Springer (2013)Google Scholar
  47. 47.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)Google Scholar
  48. 48.
    Wang, H., Yeung, D.Y.: Towards Bayesian deep learning: a survey. arXiv preprint arXiv:1604.01662 (2016)
  49. 49.
    Zaragoza, J.H., Sucar, L.E., Morales, E.F., Bielza, C., Larranaga, P.: Bayesian chain classifiers for multidimensional classification. IJCAI 11, 2192–2197 (2011)Google Scholar
  50. 50.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. (IJDWM) 3(3), 1–13 (2007)CrossRefGoogle Scholar
  51. 51.
    Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)CrossRefGoogle Scholar
  52. 52.
    Drury, B., Valverde-Rebaza, J., Moura, M.F., de Andrade Lopes, A.: A survey of the applications of Bayesian networks in agriculture. Eng. Appl. Artif. Intell. 65, 29–42 (2017)CrossRefGoogle Scholar
  53. 53.
    Weber, P., Medina-Oliva, G., Simon, C., Iung, B.: Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25(4), 671–682 (2012)CrossRefGoogle Scholar
  54. 54.
    Wiegerinck, W., Burgers, W., Kappen, B.: Bayesian networks, introduction and practical applications. In: Handbook on Neural Information Processing, pp. 401–431. Springer (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luis Gutiérrez
    • 1
  • Juan Bekios-Calfa
    • 1
  • Brian Keith
    • 1
  1. 1.Department of Computing and Systems EngineeringUniversidad Católica del NorteAntofagastaChile

Personalised recommendations