Skip to main content
SpringerLink
Log in

Identifying diverse reviews about products

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

The vast body of reviews for individual items makes it difficult for users to extract useful information. And the opinions expressed by these users can be easily influenced through the communication services provided by the E-Commerce systems. A number of works are proposed for information extraction from a large review corpora, and these techniques may release users from the tiresome task of reading reviews. However, such extracted summaries are lack of immediacy, and could not keep the reviews’ narrative structures. Aiming at enhancing the diversity of reviews and eliminating the above methods’ defects, we propose a local community based algorithm to group reviewers and recommend the original reviews to users. Our method utilizes similarity-based sparsification techniques to identify the edge types that connected two nodes to determine these two nodes are in the same community or not. Since such identification procedure only evolves the neighbors of the target nodes, it can be set on the client side, and can be accomplished efficiently. We conduct comprehensive experiments to demonstrate the accuracy of our algorithm, and provide the discussions and explanations about the phenomena appeared in the experimental results.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Notes

  1. http://www.cs.sfu.ca/sja25/personal/datasets/

  2. http://www.public.asu.edu/~jtang20/datasetcode/truststudy.htm

References

  1. Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)

    Article  Google Scholar 

  2. Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: Proceedings of the 47Th Annual IEEE Symposium on Foundations of Computer Science, FOCS ’06, pp. 475–486. IEEE Computer Society (2006)

  3. Cho, J., Schonfeld, U.: Rankmass Crawler: a Crawler with High Personalized Pagerank Coverage Guarantee. In: Proceedings of the 33Rd International Conference on Very Large Data Bases, VLDB ’07, pp. 375–386. VLDB Endowment (2007)

  4. Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72 (2), 026,132 (2005)

    Article  Google Scholar 

  5. Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Statistical Analysis and Data Mining 4(5), 512–546 (2011)

    Article  MathSciNet  Google Scholar 

  6. Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: Demon: a local-first discovery method for overlapping communities. In: Proceedings of the 18Th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 615–623. ACM (2012)

  7. Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94(16), 160,202 (2005)

    Article  Google Scholar 

  8. Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1944–1957 (2007)

    Article  Google Scholar 

  9. Estivill-Castro, V.: Why so many clustering algorithms: a position paper. SIGKDD Explor. Newsl. 4(1), 65–75 (2002)

    Article  MathSciNet  Google Scholar 

  10. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  11. Gleich, D., Seshadhri, C.: Neighborhoods are good communities. arXiv:1112.0031 (2011)

  12. Gleich, D.F., Seshadhri, C.: Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. In: Proceedings of the 18Th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12 (2012)

  13. Henderson, K., Eliassi-Rad, T., Papadimitriou, S., Faloutsos, C.: Hcdf: a hybrid community discovery framework. In: SDM, vol. 2010, pp. 754–765 (2010)

  14. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)

  15. Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142 (2010)

  16. Kannan, R., Vempala, S., Vetta, A.: On clusterings: good, bad and spectral. J. ACM (JACM) 51(3), 497–515 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Phys. Rev. E 80(5), 056,117 (2009)

    Article  Google Scholar 

  18. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: Proceedings of the 17Th International Conference on World Wide Web, WWW ’08, pp. 695–704, ACM, New York, NY, USA (2008)

  19. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  21. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  22. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web (1999)

  23. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)

    Article  Google Scholar 

  24. Zhan, J., Loh, H.T., Liu, Y.: Gather customer concerns from online product reviews: a text summarization approach. Expert Syst. Appl. 36(2), 2107–2115 (2009)

    Article  Google Scholar 

  25. Zhou, Z., Zhang, N., Gong, Z., Das, G.: Faster random walks by rewiring online social networks on-the-fly. In: Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), pp. 769–780 (2013)

  26. Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of the 15Th ACM International Conference on Information and Knowledge Management, pp. 43–50. ACM (2006)

Download references

Acknowledgments

Haitao Zou,Zhiguo Gong, and Weishu Hu were supported in part by Fund of Science and Technology Development of Macau Government under FDCT/106/2012/A3 and FDCT/116/2013/A3; and in part by University Macau Research Committee under MYRG105-FST13-GZG and MYRG2015-00070-FST. Haitao Zou was also supported in part by the High Level Talents Scientific Research Fund in Jiangsu University of Science and Technology under grants 1132921506.

Any opinions, findings, conclusions, and/or recommendations expressed in this material, either expressed or implied, are those of the authors and do not necessarily reflect the views of the sponsors listed above.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

    Haitao Zou

  2. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China

    Zhiguo Gong

  3. Jiangnan University, Wuxi, China

    Weishu Hu

Authors
  1. Haitao Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiguo Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weishu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haitao Zou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, H., Gong, Z. & Hu, W. Identifying diverse reviews about products. World Wide Web 20, 351–369 (2017). https://doi.org/10.1007/s11280-016-0391-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-016-0391-3

Keywords

Search

Navigation