Skip to main content
Springer Nature Link
Log in

AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Aspect-level opinion mining systems suffer from concept coverage problem due to the richness and ambiguity of natural language opinions. Aspects mentioned by review authors can be expressed in various forms, resulting in a potentially large number of missing or incomplete aspects. This work proposes a novel unsupervised method to extract aspects from raw reviews with a broader coverage. Previous research has shown that unsupervised methods based on dependency relations are promising for opinion target extraction (OTE). In this work, we introduce Aspect Network (AspNet), an AspNet that further improves existing OTE methods by providing a new framework for modeling aspects. AspNet represents the general indecomposable atom aspects and their dependency relations in a two-layered, directed, weighted graph, based on which the specific decomposable compound aspects in reviews can be effectively extracted. AspNet is constructed through an unsupervised learning method that starts from a small number of human-defined, domain-dependent aspects, and bootstraps generalization and propagation in a large volume of raw reviews. In summary, the major contributions of this work are twofold: Firstly, the proposed AspNet is a new framework in modeling aspects; secondly, an unsupervised method is proposed to construct AspNet in a bootstrapping manner within raw reviews to learn aspects automatically. Experimental results demonstrate that our proposed OTE method, based on AspNet, can achieve significant gains over baseline methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Cai Q, He H, Man H. Imbalanced evolving self-organizing learning. Neurocomputing. 2014;133:258–70.

    Article  Google Scholar 

  2. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Dordrecht: Springer; 2012.

    Book  Google Scholar 

  3. Cambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 2013;28(2):15–21.

    Article  Google Scholar 

  4. Das D, Bandyopadhyay S. Sentence-level emotion and valence tagging. Cogn. Comput. 2012;4(4):420–35.

    Article  Google Scholar 

  5. Dong Z, Dong Q. Hownet and the computation of meaning. River Edge: World Scientific Publishing Co., Inc.; 2006.

    Book  Google Scholar 

  6. Ghani R, Probst K, Liu Y, Krema M, Fano A. Text mining for product attribute extraction. SIGKDD Explor. Newsl. 2006;8(1):41–8.

    Article  Google Scholar 

  7. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining., KDD ’04New York, NY, USA: ACM; 2004. p. 168–77.

  8. Hu M, Liu B. Mining opinion features in customer reviews. In: Proceedings of the 19th national conference on artificial intelligence, AAAI’04. AAAI Press; 2004. p. 755–60

  9. Jakob N, Gurevych I. Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of the 2010 conference on empirical methods in natural language processing., EMNLP ’10Stroudsburg, PA, USA: Association for Computational Linguistics; 2010. p. 1035–45.

  10. Jin W, Ho HH. A novel lexicalized hmm-based learning framework for web opinion miningnote from acm: a joint acm conference committee has been determined that the authors of this article violated acm’s publication policy on simultaneous submissions. Therefore acm has shut off access to this paper. In: Proceedings of the 26th annual international conference on machine learning, ICML’09New York, NY, USA: ACM; 2009. p. 465–72.

  11. Kobayashi N, Inui K, Matsumoto Y. Extracting aspect-evaluation and aspect-of relations in opinion mining. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL, EMNLP-CoNLL’07. Association for Computational Linguistics; 2007. p. 1065–74

  12. Li F, Huang M, Zhu X. Sentiment analysis with global topics and local dependency. In: Fox M, Poole D, editors. Proceedings of the twenty-fourth conference on artificial intelligence, AAAI ’10. AAAI Press; 2010

  13. Ma J, Zhang Y, Liu T, Li S. A statistical dependency parser of chinese under small training data. In: Proceedings of workshop: beyond shallow analyses-formalisms and statistical modeling for deep analyses, IJCNLP’04; 2004. p. 1–5.

  14. Popescu AM, Etzioni O. Extracting product features and opinions from reviews. In: Proceedings of the conference on human language technology and empirical methods in natural language processing, HLT ’05Stroudsburg, PA, USA: Association for Computational Linguistics; 2005. p. 339–46.

  15. Poria S, Cambria E, Ku L-WCG, Gelbukh A. In: Proceedings of the 25th international conference on computational linguistics.

  16. Poria S, Cambria E, Winterstein G, Huang GB. Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems 2014.

  17. Qiu G, Liu B, Bu J, Chen C. Opinion word expansion and target extraction through double propagation. Comput. Linguist. 2011;37(1):9–27. doi:10.1162/coli_a_00034.

    Article  Google Scholar 

  18. Seki Y, Evans DK, Ku LW, Chen HH, Kando N, Lin CY. Overview of opinion analysis pilot task at NTCIR-6. In: Proceedings of the workshop meeting of the National Institute of Informatics (NII) test collection for information retrieval systems (NTCIR), 2007. p. 265–78.

  19. Sun R. Moral judgment, human motivation, and neural networks. Cogn. Comput. 2013;5(4):566–79.

    Article  Google Scholar 

  20. Xu R, Xia Y, Wong KF, Li W. Opinion annotation in on-line chinese product reviews. In: LREC 2008.

  21. Yu J, Zha ZJ, Wang M, Chua TS. Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol. 1, HLT’11Stroudsburg, PA, USA: Association for Computational Linguistics; 2011. p. 1496–505.

  22. Zhu J, Wang H, Tsou BK, Zhu M. Multi-aspect opinion polling from textual reviews. In: Proceedings of the 18th ACM conference on information and knowledge management, CIKM ’09New York, NY, USA: ACM; 2009. p. 1799–802.

Download references

Acknowledgments

Research work in this paper is partially supported by NSFC (No. 61272233). We thank the anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. Department of Computer Science, TNList, Tsinghua University, Beijing, 100084, China

    Yunqing Xia

  2. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Erik Cambria

  3. Division of Computing Science and Maths, School of Natural Science, University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Amir Hussain

Authors
  1. Yunqing Xia
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Erik Cambria
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Amir Hussain
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yunqing Xia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Y., Cambria, E. & Hussain, A. AspNet: Aspect Extraction by Bootstrapping Generalization and Propagation Using an Aspect Network. Cogn Comput 7, 241–253 (2015). https://doi.org/10.1007/s12559-014-9305-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-014-9305-9

Keywords