Advertisement

High Resolution Sentiment Analysis by Ensemble Classification

  • Jordan J. BirdEmail author
  • Anikó Ekárt
  • Christopher D. Buckingham
  • Diego R. Faria
Conference paper
  • 278 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 997)

Abstract

This study proposes an approach to ensemble sentiment classification of a text to a score in the range of 1–5 of negative-positive scoring. A high-performing model is produced from TripAdvisor restaurant reviews via a generated dataset of 684 word-stems, gathered by information gain attribute selection from the entire corpus. The best performing classification was an ensemble classifier of RandomForest, Naive Bayes Multinomial and Multilayer Perceptron (Neural Network) methods ensembled via a Vote on Average Probabilities approach. The best ensemble produced a classification accuracy of 91.02% which scored higher than the best single classifier, a Random Tree model with an accuracy of 78.6%. Other ensembles through Adaptive Boosting, Random Forests and Voting are explored with ten-fold cross-validation. All ensemble methods far outperformed the best single classifier methods. Even though extremely high results are achieved, analysis documents the few mis-classified instances as almost entirely being close to their real class via the model’s given error matrix.

Keywords

Sentiment analysis Opinion mining Machine learning Ensemble learning Classification 

Notes

Acknowledgments

This work was supported by the European Commission through the H2020 project EXCELL (https://www.excell-project.eu/), grant No. 691829.

This work was also partially supported by the EIT Health GRaCEAGE grant number 18429 awarded to C.D. Buckingham.

References

  1. 1.
    Adama, D.A., Lotfi, A., Langensiepen, C.: Key frame extraction and classification of human activities using motion energy. In: UK Workshop on Computational Intelligence, pp. 303–311. Springer (2018)Google Scholar
  2. 2.
    Bayes, T.: An essay towards solving a problem in the doctrine of chances (1763). By the late Reviewed: R. Price, J. CantonGoogle Scholar
  3. 3.
    Bird, J.J., Ekárt, A., Buckingham, C.D., Faria, D.R.: Mental emotional sentiment classification with an EEG-based brain-machine interface. In: The International Conference on Digital Image and Signal Processing (DISP 2019). Springer (2019)Google Scholar
  4. 4.
    Bird, J.J., Ekárt, A., Faria, D.R.: Learning from interaction: an intelligent networked-based human-bot and bot-bot chatbot system. In: UK Workshop on Computational Intelligence, pp. 179–190. Springer (2018)Google Scholar
  5. 5.
    Bird, J.J., Manso, L.J., Ribiero, E.P., Ekárt, A., Faria, D.R.: A study on mental state classification using EEG-based brain-machine interface. In: 9th International Conference on Intelligent Systems. IEEE (2018)Google Scholar
  6. 6.
    Bollegala, D., Weir, D., Carroll, J.: Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 132–141. Association for Computational Linguistics (2011)Google Scholar
  7. 7.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  8. 8.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)Google Scholar
  9. 9.
    Cui, H., Mittal, V., Datar, M.: Comparative experiments on sentiment classification for online product reviews. In: AAAI, vol. 6, pp. 1265–1270 (2006)Google Scholar
  10. 10.
    Denecke, K.,: Are SentiWordNet scores suited for multi-domain sentiment classification? In: 2009 Fourth International Conference on Digital Information Management, ICDIM 2009, pp. 1–6. IEEE (2009)Google Scholar
  11. 11.
    Faria, D.R., Vieira, M., Faria, F.C.C., Premebida, C.: Affective facial expressions recognition for human-robot interaction. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 805–810. IEEE (2017)Google Scholar
  12. 12.
    Faria, D.R., Vieira, M., Faria, F.C.C.: Towards the development of affective facial expression recognition for human-robot interaction. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 300–304. ACM (2017)Google Scholar
  13. 13.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)Google Scholar
  14. 14.
    Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)Google Scholar
  15. 15.
    Ho, T.K.: Random decision forests. In: 1995 Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  16. 16.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, Montreal, Canada, vol. 14, pp. 1137–1145 (1995)Google Scholar
  17. 17.
    Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the OMG!. Icwsm 11(538–541), 164 (2011)Google Scholar
  18. 18.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)Google Scholar
  19. 19.
    Lee, C.-W., Wang, Y.-S., Hsu, T.-Y., Chen, K.-Y., Lee, H.-Y., Lee, L.-S.: Scalable sentiment for sequence-to-sequence chatbot response with performance analysis. arXiv preprint arXiv:1804.02504 (2018)
  20. 20.
    Lisetti, C.L.: Affective computing. Pattern Anal. Appl. 1(1), 71–73 (1998)Google Scholar
  21. 21.
    Lovins, J.B.: Development of a stemming algorithm. Mech. Transl. Comput. Linguist. 11, 22–31 (1968)Google Scholar
  22. 22.
    Lu, B., Ott, M., Cardie, C., Tsou, B.K.: Multi-aspect sentiment analysis with topic models. In: 2011 11th IEEE International Conference on Data Mining Workshops, pp. 81–88. IEEE (2011)Google Scholar
  23. 23.
    McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. Citeseer (1998)Google Scholar
  24. 24.
    McCallum, A.K.: Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering (1996). http://www.cs.cmu.edu/mccallum/bow
  25. 25.
    Nogueiras, A., Moreno, A., Bonafonte, A., Mariño, J.B.: Speech emotion recognition using hidden Markov models. In: Seventh European Conference on Speech Communication and Technology (2001)Google Scholar
  26. 26.
    Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)Google Scholar
  27. 27.
    Prasad, A.M., Iverson, L.R., Liaw, A.: Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2), 181–199 (2006)Google Scholar
  28. 28.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)Google Scholar
  29. 29.
    Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)Google Scholar
  30. 30.
    Rojas, R.: AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Technical report, Freie University, Berlin (2009)Google Scholar
  31. 31.
    Rosenblatt, F.: Principles of neurodynamics. Perceptrons and the theory of brain mechanisms. Technical report, Cornell Aeronautical Lab Inc., Buffalo, NY (1961)Google Scholar
  32. 32.
    Schalkoff, R.J.: Artificial Neural Networks, vol. 1. McGraw-Hill, New York (1997)Google Scholar
  33. 33.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)Google Scholar
  34. 34.
    Valdivia, A., Luzón, M.V., Herrera, F.: Sentiment analysis in TripAdvisor. IEEE Intell. Syst. 32(4), 72–77 (2017)Google Scholar
  35. 35.
    Vieira, M., Faria, D.R., Nunes, U.: Real-time application for monitoring human daily activity and risk situations in robot-assisted living. In: Robot 2015: Second Iberian Robotics Conference, pp. 449–461. Springer (2016)Google Scholar
  36. 36.
    Zhang, C., Zhang, S.: Association Rule Mining: Models and Algorithms. Springer, Berlin (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jordan J. Bird
    • 1
    Email author
  • Anikó Ekárt
    • 1
  • Christopher D. Buckingham
    • 1
  • Diego R. Faria
    • 1
  1. 1.School of Engineering and Applied ScienceAston UniversityBirminghamUK

Personalised recommendations