Skip to main content
SpringerLink
Log in
Book cover

Advances in Social Media Analysis pp 105–127Cite as

Case-Studies in Mining User-Generated Reviews for Recommendation

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 602))

Abstract

User-generated reviews are now plentiful online and they have proven to be a valuable source of real user opinions and real user experiences. In this chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from filtering helpful reviews to recommending useful products.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    OpenNLP: http://incubator.apache.org/opennlp/.

  2. 2.

    In long sentences, users may comment on multiple features. Thus, we introduce a window size for negation terms to limit their scope to nearby features. Based on experiment, we set the window size to four. Moreover, we identify certain phrases (e.g. “not only”) which are not considered from a sentiment perspective. We acknowledge that more sophisticated sentiment analysis techniques have been proposed, an investigation of which we leave to future work.

References

  1. Hu, N., Liu, L., Zhang, J.: Inf. Technol. Manag. 9, 201 (2008)

    Article  Google Scholar 

  2. Zhu, F., Zhang, X.M.: J. Market. 74(2), 133 (2010)

    Article  Google Scholar 

  3. Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pp. 423–430. Sydney, Australia (2006)

    Google Scholar 

  4. Liu, Y., Huang, X., An, A., Yu, X. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 443–452. IEEE Computer Society, Pisa, Italy (2008)

    Google Scholar 

  5. Baccianella, S., Esuli, A., Sebastiani, F. In: Advances in Information Retrieval, 31th European Conference on Information Retrieval Research (ECIR 2009), pp. 461–472. Springer, Toulouse, France (2009)

    Google Scholar 

  6. Hsu, C.F., Khabiri, E., Caverlee, J. In: Proceedings of the 2009 IEEE International Conference on Social Computing (SocialCom 2009), pp. 90–97. Vancouver, Canada (2009)

    Google Scholar 

  7. O’Mahony, M.P., Cunningham, P., Smyth, B. In: Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2009), pp. 244–253. Dublin, Ireland (2009)

    Google Scholar 

  8. O’Mahony, M.P., Smyth, B. In: Proceedings of the 3rd ACM Conference on Recommender Systems, RecSys ’09. New York (2009)

    Google Scholar 

  9. Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 939–948. ACM, New York (2010). doi:10.1145/1871437.1871557. http://doi.acm.org/10.1145/1871437.1871557

  10. Li, F., Huang, M., Yang, Y., Zhu, X. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence—Volume Volume Three, IJCAI 2011, pp. 2488–2493. AAAI Press, San Jose (2011). doi:10.5591/978-1-57735-516-8/IJCAI11-414. http://dx.doi.org/10.5591/978-1-57735-516-8/IJCAI11-414

  11. Chevalier, J.A., Dina Mayzlin, D.: J. Market. Res. 43(3), 345 (2006)

    Google Scholar 

  12. Dhar, V., Chang, E.A.: J. Interact. Market. 23(4), 300 (2009)

    Article  Google Scholar 

  13. Dellarocas, C., Zhang, M., Awad, N.F.: J. Interact. Market. 21(4), 23 (2007)

    Article  Google Scholar 

  14. O’Callaghan, D., Harrigan, M., Carthy, J., Cunningham, P. In: ICWSM (2012)

    Google Scholar 

  15. Popescu, A.M., Etzioni, O. In: Natural Language Processing and Text Mining, pp. 9–28. Springer, London, 2007. doi:10.1007/978-1-84628-754-12. http://dx.doi.org/10.1007/978-1-84628-754-1_2

  16. Hu, M., Liu, B. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pp. 168–177. ACM, New York (2004). doi:10.1145/1014052.1014073. http://doi.acm.org/10.1145/1014052.1014073

  17. Hu, M., Liu, B. In: Proceedings of the 19th National Conference on Artifical Intelligence, AAAI’04, pp. 755–760. AAAI Press, San Jose (2004). http://dl.acm.org/citation.cfm?id=1597148.1597269

  18. Zhang, K., Narayanan, R., Choudhary, A. In: Proceedings of the 3rd Workshop on Online Social Networks, WOSN ’10. Berkeley (2010). http://dl.acm.org/citation.cfm?id=1863190.1863201

  19. Garcia Esparza, S., O’Mahony, M.P., Smyth, B. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys ’10, pp. 305–308. ACM, New York (2010). doi:10.1145/1864708.1864773. http://doi.acm.org/10.1145/1864708.1864773

  20. De Francisci Morales, G., Gionis, A., Lucchese, C. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining, WSDM ’12, pp. 153–162. ACM, New York (2012). doi:10.1145/2124295.2124315. http://doi.acm.org/10.1145/2124295.2124315

  21. Poirier, D., Tellier, I., Fessant, F., Schluth, J. In: Adaptivity, Personalization and Fusion of Heterogeneous Information, RIAO ’10, pp. 136–137. Paris, France (2010). doi:http://dl.acm.org/citation.cfm?id=1937055.1937089

  22. Justeson, J.S., Katz, S.M.: Nat. Lang. Eng. 1(1), 9 (1995)

    Article  Google Scholar 

  23. Qiu, G., Liu, B., Bu, J., Chen, C. In: Proceedings of the 21st International Joint Conference on Artifical Intelligence, IJCAI 2009, pp. 1199–1204. Morgan Kaufmann Publishers Inc., San Francisco (2009). http://dl.acm.org/citation.cfm?id=1661445.1661637

  24. Moghaddam, S., Ester, M. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 1825–1828. ACM, New York (2010). doi:10.1145/1871437.1871739. http://doi.acm.org/10.1145/1871437.1871739

  25. DuBay, W. Impact Information, pp. 1–76 (2004)

    Google Scholar 

  26. O’Mahony, M.P., Smyth, B. In: Adaptivity, Personalization and Fusion of Heterogeneous Information, RIAO 2010, pp. 164–167. Paris, France (2010). http://dl.acm.org/citation.cfm?id=1937055.1937097

  27. Witten, I., Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Diego (2005)

    Google Scholar 

  28. Streiner, D., Cairney, J. The Canadian Journal of Psychiatry/La revue Canadienne de Psychiatrie (2007)

    Google Scholar 

  29. Pazzani, M., Billsus, D. In: The Adaptive Web, Lecture Notes in Computer Science, vol. 4321, pp. 325–341. Springer, Berlin, Heidelberg (2007). doi:10.1007/978-3-540-72079-910. http://dx.doi.org/10.1007/978-3-540-72079-9_10

  30. Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B. In: Proceedings of the 20th International Conference on Case-Based Reasoning (2012), ICCBR ’12, pp. 62–76

    Google Scholar 

  31. Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B. In: Proceedings of the 21st International Conference on Case-Based Reasoning, ICCBR ’13, vol. 7969, pp. 44–58. Springer, Heidelberg (2013)

    Google Scholar 

  32. Dong, R., Schaal, M., O’Mahony, M.P., Smyth, B. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI ’13, pp. 1310–1316. AAAI Press, Menlo Park, California (2013)

    Google Scholar 

  33. Dong, R., O’Mahony, M.P., Schaal, M., McCarthy, K., Smyth, B. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys ’13, pp. 411–414. ACM, New York (2013). doi:10.1145/2507157.2507199. http://doi.acm.org/10.1145/2507157.2507199

Download references

Acknowledgments

This work is supported by Science Foundation Ireland: through the CLARITY Centre for Sensor Web Technologies under grant number 07/CE/I1147; and through the Insight Centre for Data Analytics under grant number SFI/12/RC/2289.

Author information

Authors and Affiliations

  1. CLARITY: Centre for Sensor Web Technologies, University College Dublin, Dublin, Ireland

    Ruihai Dong

  2. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Michael P. O’Mahony, Kevin McCarthy & Barry Smyth

Authors
  1. Ruihai Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael P. O’Mahony
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin McCarthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barry Smyth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruihai Dong .

Editor information

Editors and Affiliations

  1. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Mohamed Medhat Gaber

  2. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Mihaela Cocea

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Nirmalie Wiratunga

  4. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Ayse Goker

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Dong, R., O’Mahony, M.P., McCarthy, K., Smyth, B. (2015). Case-Studies in Mining User-Generated Reviews for Recommendation. In: Gaber, M., Cocea, M., Wiratunga, N., Goker, A. (eds) Advances in Social Media Analysis. Studies in Computational Intelligence, vol 602. Springer, Cham. https://doi.org/10.1007/978-3-319-18458-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18458-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18457-9

  • Online ISBN: 978-3-319-18458-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation