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International Conference on Machine Learning, Optimization, and Data Science

LOD 2020: Machine Learning, Optimization, and Data Science pp 285–296Cite as

Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

Nowadays, the exponential diffusion of information forces Machine Learning algorithms to take relations into account in addition to data, which are no longer independent. We propose a Bayesian ensemble learning methodology named Relational Bayesian Model Averaging (RBMA) which, in addition to a probabilistic ensemble voting, takes relations into account. We tested the RBMA on a benchmark dataset for Sentiment Analysis in social networks and we compared it with its previous non-relational variant and we show that the introduction of relations significantly improves the performance of classification. Moreover, we propose a model for making predictions when new data becomes available modifying and increasing the underneath graph of relations on which the RBMA was trained.

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References

  1. Weitz, S.: Search: How the Data Explosion Makes Us Smarter. GreenHouse Collection. Routledge, London (2014)

    Google Scholar 

  2. Fersini, E., Pozzi, F.A., Messina, E.: Approval network: a novel approach for sentiment analysis in social networks. World Wide Web 20(4), 831–854 (2016). https://doi.org/10.1007/s11280-016-0419-8

    Article  Google Scholar 

  3. Lu, Q., Getoor, L.: Link-based classification. In: ICML 2003: Proceedings of the Twentieth International Conference on International Conference on Machine Learning, pp. 496–503 (2003)

    Google Scholar 

  4. Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1), 1–39 (2010)

    Article  MathSciNet  Google Scholar 

  5. Fersini, E., Messina, E., Pozzi, F.A.: Sentiment analysis: Bayesian ensemble learning. Decis. Support Syst. 68, 26–38 (2014)

    Article  Google Scholar 

  6. Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)

    Article  Google Scholar 

  7. Mooney, R.: Statistical relational learning and script induction for textual inference. Technical report AFRL-RI-RS-TR-2017-243, Air force research laboratory information dictorate (2017)

    Google Scholar 

  8. Ramanan, N., et al.: Structure learning for relational logistic regression: an ensemble approach. In: Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018), pp. 661–662 (2018)

    Google Scholar 

  9. Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Computing Surveys 50(2), 1–36 (2017). Article 23

    Article  Google Scholar 

  10. Sagi, O.: L-Rokach: ensemble learning: a survey. WIREs Data Min. Knowl. Discov. (2018). https://doi.org/10.1002/widm.1249

  11. Preisach, C., Schmidt-Thieme, L.: Ensembles of relational classifiers. Knowl. Inf. Syst. 14(3), 249–272 (2008)

    Article  Google Scholar 

  12. Li, Y., Zhong, S., Zhong, Q., Shi, K.: Lithium-ion battery state of health monitoring based on ensemble learning. IEEE Access 7, 8754–8762 (2019)

    Article  Google Scholar 

  13. Bablani, A., Edla, D.R., Tripathi, D., Kuppili, V.: An efficient concealed information test: EEG feature extraction and ensemble classification for lie identification. Mach. Vis. Appl. 30(5), 813–832 (2019)

    Article  Google Scholar 

  14. Alfred, R., Shin, K.K., Chin, K.O., Lau, H.K., Hijazi, M.H.A.: k-NN ensemble DARA approach to learning relational. In: Abawajy, J.H., Othman, M., Ghazali, R., Deris, M.M., Mahdin, H., Herawan, T. (eds.) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015). LNEE, vol. 520, pp. 203–212. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1799-6_22

    Chapter  Google Scholar 

  15. Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial. Stat. Sci. 14(4), 382–401 (1999)

    Article  MathSciNet  Google Scholar 

  16. Wasserman, L.: Bayesian model selection and model averaging. J. Math. Psychol. 44, 92–107 (2000)

    Article  MathSciNet  Google Scholar 

  17. Raftery, A.E., Gneiting, T., Balabdaoui, F., Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev. 133(5), 1155–1174 (2005)

    Article  Google Scholar 

  18. Pozzi, F.A., Fersini, E., Messina, E.: Bayesian model averaging and model selection for polarity classification. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) NLDB 2013. LNCS, vol. 7934, pp. 189–200. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38824-8_16

    Chapter  Google Scholar 

  19. Pozzi, F.A., Maccagnola, D., Fersini, E., Messina, E.: Enhance user-level sentiment analysis on microblogs with approval relations. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS (LNAI), vol. 8249, pp. 133–144. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03524-6_12

    Chapter  Google Scholar 

  20. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  21. Jurafsky, D., Martin, J.H.: Speech and Language Processing. Series in Artificial Intelligence. Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  22. Macskassy, S.A., Provost, F.: Classification in networked data: a toolkit and a univariate case study. J. Mach. Learn. Res. 8, 935–983 (2007)

    Google Scholar 

  23. Swamynathan, M.: Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python (2017)

    Google Scholar 

  24. Università degli Studî di Milano-Bicocca: The MIND laboratory. http://www.mind.disco.unimib.it/gallery/index.asp?cat=92&level=1&lang=en

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Acknowledgements

This work has been partially funded by MISE (Ministero Italiano dello Sviluppo Economico) under the project “SMARTCAL – Smart Tourism in Calabria” (F/050142/01-03/x32).

Moreover, the authors are very grateful to Sofus A. Macskassy for his generosity in helping with the NetKit toolkit.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Milano-Bicocca, Milan, Italy

    Mauro Maria Baldi, Elisabetta Fersini & Enza Messina

Authors
  1. Mauro Maria Baldi
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  2. Elisabetta Fersini
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  3. Enza Messina
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Corresponding author

Correspondence to Mauro Maria Baldi .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Baldi, M.M., Fersini, E., Messina, E. (2020). Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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