WS4ABSA: An NMF-Based Weakly-Supervised Approach for Aspect-Based Sentiment Analysis with Application to Online Reviews

  • Alberto PurpuraEmail author
  • Chiara MasieroEmail author
  • Gian Antonio SustoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)


The goal of Aspect-Based Sentiment Analysis is to identify opinions regarding specific targets and the corresponding sentiment polarity in a document. The proposed approach is designed for real-world scenarios, where the amount of available information and annotated data is often too limited to train supervised models. We focus on the two core tasks of Aspect-Based Sentiment Analysis: aspect and sentiment polarity classification. The first task – which consists in the identification of the opinion targets in a document – is tackled by means of a weakly-supervised technique based on Non-negative Matrix Factorization. This strategy allows users to easily embed some a priori domain knowledge by means of short seed terms lists. Experimental results on publicly available data sets related to online reviews suggest that the proposed approach is very flexible and can be easily adapted to different languages and domains.


Aspect-based sentiment analysis Non-negative matrix factorization Text mining Weakly-supervised learning 


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812. Association for Computational Linguistics (2010)Google Scholar
  3. 3.
    Choo, J., Lee, C., Reddy, C.K., Park, H.: Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Vis. Comput. Graph. 19(12), 1992–2001 (2013)CrossRefGoogle Scholar
  4. 4.
    Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron., Commun. Comput. Sci. 92(3), 708–721 (2009)CrossRefGoogle Scholar
  5. 5.
    García-Pablos, A., Cuadros, M., Rigau, G.: W2vlda: almost unsupervised system for aspect based sentiment analysis. Expert. Syst. Appl. 91, 127–137 (2018)CrossRefGoogle Scholar
  6. 6.
    Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kim, J., He, Y., Park, H.: Algorithms for nonnegative matrix and tensor factorizations: A unified view based on block coordinate descent framework. J. Glob. Optim. 58(2), 285–319 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kuang, D., Choo, J., Park, H.: Nonnegative matrix factorization for interactive topic modeling and document clustering. In: Celebi, M.E. (ed.) Partitional Clustering Algorithms, pp. 215–243. Springer, Cham (2015). Scholar
  9. 9.
    Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems, vol. 15. SIAM, Philadelphia (1995)CrossRefGoogle Scholar
  10. 10.
    Li, T., Sindhwani, V., Ding, C., Zhang, Y.: Bridging domains with words: Opinion analysis with matrix tri-factorizations. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 293–302. SIAM (2010)Google Scholar
  11. 11.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  12. 12.
    Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-vol. 1, pp. 142–150. Association for Computational Linguistics (2011)Google Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  14. 14.
    Paatero, P., Tapper, U.: Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994)CrossRefGoogle Scholar
  15. 15.
    Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016)Google Scholar
  16. 16.
    Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015)Google Scholar
  17. 17.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  18. 18.
    Toh, Z., Su, J.: Nlangp at semeval-2016 task 5: improving aspect based sentiment analysis using neural network features. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 282–288 (2016)Google Scholar
  19. 19.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2006)CrossRefGoogle Scholar
  20. 20.
    Varghese, R., Jayasree, M.: Aspect based sentiment analysis using support vector machine classifier. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1581–1586. IEEE (2013)Google Scholar
  21. 21.
    Vavasis, S.A.: On the complexity of nonnegative matrix factorization. SIAM J. Optim. 20(3), 1364–1377 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wang, F., Li, T., Zhang, C.: Semi-supervised clustering via matrix factorization. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 1–12. SIAM (2008)Google Scholar
  23. 23.
    Xiang, B., Zhou, L.: Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 434–439 (2014)Google Scholar
  24. 24.
    Yan, X., Guo, J., Liu, S., Cheng, X., Wang, Y.: Learning topics in short texts by non-negative matrix factorization on term correlation matrix. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 749–757. SIAM (2013)CrossRefGoogle Scholar
  25. 25.
    Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pp. 1073–1080. Association for Computational Linguistics (2008)Google Scholar
  26. 26.
    Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a maxent-lda hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65. Association for Computational Linguistics (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of PadovaPadovaItaly

Personalised recommendations