Abstract
In this paper, we present a deep architecture to perform aspect-level sentiment analysis for news articles. We combine some neural networks models proposed in various deep learning approaches, aiming at tackling specific issues commonly occurring for news articles. In this paper, we explain why our architecture can handle typically-long and content-specific news articles, which often cause overfitting when trained with neural networks. Moreover, the proposed architecture can also effectively process the case when the subject to be analyzed sentimentally is not the main topic of the concerned article, which is also a common issue when performing aspect-level sentiment processing. Experimental results with real dataset demonstrated advantages of our approach as compared to the existing approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003), Sanibel Island, FL, USA, 23–25 October 2003. pp. 70–77 (2003)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the Twelfth International World Wide Web Conference, WWW 2003, Budapest, Hungary, 20–24 May 2003, pp. 519–528 (2003)
Padmaja, S., Fatima, S.S.: Opinion mining and sentiment analysis-an assessment of peoples’ belief: a survey. Int. J. Ad hoc Sensor Ubiquit. Comput. 4(1), 21 (2013)
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Rafael (2012)
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)
Mei, Q., Ling, X.,Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, 8–12 May 2007, pp. 171–180 (2007)
Li, F., Huang, M., Zhu, X.: Sentiment analysis with global topics and local dependency. In: Proceedings of the Twenty-Fourth AAAI Conference on Artifcial Intelligence, AAAI 2010, 11–15 July 2010, Atlanta, Georgia, USA (2010)
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, EMNLP 2010, MIT Stata Center, Massachuset, 9–11 October 2010
Sauper, C., Haghighi, A., Barzilay, R.: Content models with attitude. In: Proceedings of the Conference the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 19–24 June 2011, pp. 350–358 (2011)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the Conference on the 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, Korea, 8–14 July 2012, vol. 1, pp. 339–348 (2012). LongPapers
Titov, I., McDonald, R.T.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, ACL 2008, Columbus, Ohio, USA, 15–20 June 2008, pp. 308–316 (2008)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the Conference on 43rd Annual Meeting of the Association for Computational Linguistics, ACL 2005, University of Michigan, USA, 25–30 June 2005, pp. 115–124 (2005)
Le Cun, Y.: Generalization and network design strategies. Technical Report CRG-TR-89-4, University of Toronto Connectionist Research Group, June 1989. A shorter version was published in Pfeifer, Schreter, Fogelman and Steels (eds.) ‘Connectionism in perspective’. Elsevier (1989)
Hinton, G.E.: Mapping part-whole hierarchies into connectionist networks. Artif. Intell. 46(1–2), 47–75 (1990)
Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting, Lake Tahoe, Nevada, United States, 3–6 December 2012, pp. 3176–3184 (2012)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, Baltimore, MD, USA, 22–27 June 2014, System Demonstrations, pp. 55–60 (2014)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), vol. 1631, p. 1642. Citeseer (2013)
Kim, Y.: Convolutional neural networks for sentence classification. CoRRabs/1408.5882 (2014)
Wang, X., Jiang, W., Luo, Z.: Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: 26th International Conference on Computational Linguistics, COLING 2016, Proceedings of the Conference: Technical Papers, Osaka, Japan, 11–16 December 2016, pp. 2428–2437 (2016)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Gers, F.: Long short-term memory in recurrent neural networks. Ph.D. thesis, Universitat Hannover (2001)
Medsker, L., Jain, L.: Recurrent Neural Networks: Design and Applications. CRC Press, Inc., Boca Raton (2001)
Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed 18 Feb 2017
Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEETrans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)
Batista, G.E.A.P.A., Bazzan, A.L.C., Monard, M.C.: Balancing training data forautomated annotation of keywords: a case study. In: II Brazilian Workshop on Bioinformatics, Macáe, RJ, Brazil, 3–5 December 2003, pp. 10–18 (2003)
Acknowledgments
This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number C2016-20-36. We are also grateful to YouNet Media for supporting real datasets for our experiment.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Nguyen, D., Vo, K., Pham, D., Nguyen, M., Quan, T. (2018). A Deep Architecture for Sentiment Analysis of News Articles. In: Le, NT., van Do, T., Nguyen, N., Thi, H. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2017. Advances in Intelligent Systems and Computing, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-61911-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-61911-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61910-1
Online ISBN: 978-3-319-61911-8
eBook Packages: EngineeringEngineering (R0)