Skip to main content
SpringerLink
Log in

Summarization of User Reviews on E-Commerce Websites Using Hidden Markov Model

  • Conference paper
  • First Online:
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018) (ICCBI 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 31))

Included in the following conference series:

  • 1827 Accesses

Abstract

E-commerce is one of the most important reasons for the growth and sustenance of the internet, with the number of regular users of these sites increasing day by day. The user review forums on e-commerce websites help to get an overview about products of interest. However these reviews are not very organized with respect to whether features of the product, or service provided by the dealers is under discussion. These product reviews can further be positive, negative or a combination of both. This paper discusses a solution to the problem through summarization of product reviews across popular e-commerce websites. The Hidden Markov Model is used to find the component-feature pair, and the SentiWordNet libraries for Weight and Polarity Assignment. After aggregation, the final output is a graph which lists the top six aspects of the product discussed in the review forums along with the weighted polarity representation.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  1. Chen, L., Chen, G., Wang, F.: Recommender Systems Based on User Reviews: The State of the Art. Springer, Dordrecht (2015). https://doi.org/10.1007/s11257-015-9155-5

    Book  Google Scholar 

  2. Novotny, R., Vojtas, P., Maruscak, D.: Information extraction from web pages. In: WI-IAT 2009: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (2009)

    Google Scholar 

  3. Chainapaporn, P., Netisopakul, P.: Thai herb information extraction from multiple websites. In: 4th International Conference Knowledge and Smart Technology (KST) (2012)

    Google Scholar 

  4. Tripathy, A.K., Sundararajan, R., Deshpande, C., Mishra, P., Natarajan, N.: Opinion mining from user reviews. In: International Conference on Technologies for Sustainable Development (ICTSD-2015), Feb. 04–06, Mumbai, India (2015)

    Google Scholar 

  5. Raut, V.B, Londhe, D.D.: Opinion mining and summarization of hotel reviews. In: 2014 International Conference on Year: 2014 Computational Intelligence and Communication Networks (CICN) (2014)

    Google Scholar 

  6. Deepak, T., Singh, J.: The SAFE miner: a fine grained aspect level approach for resolving the sentiment. In: Proceedings of the 2015 Third International Conference on Computer Communication Control and Information Technology (C3IT) (2015)

    Google Scholar 

  7. Vidhya, K.A., Soorya, R., Saranavan, N., Geetha, T.V., Singaravelan, M.: Entity resolution for symptom vs disease for top-K treatments. In: 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) (2017)

    Google Scholar 

  8. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retriev. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  9. Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17(2007), 1–26 (2007)

    Google Scholar 

  10. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: AAAI 2004: Proceedings of the 19th National conference on Artificial intelligence, vol. 4, no. 4, pp. 755–760 (2004)

    Google Scholar 

  11. Precision and Recall. https://en.wikipedia.org/wiki/Precision_and_recall

  12. Stanford Log-linear Part-Of-Speech Tagger. https://nlp.stanford.edu/software/tagger.shtml

  13. Jaunt. https://jaunt-api.com/

  14. FlipKart. www.flipkart.com/

  15. Amazon. https://www.amazon.com/

Download references

Author information

Authors and Affiliations

  1. College of Engineering, Department of Computer Science and Engineering, Anna University, Chennai, India

    T. Pradeep, M. R. Sai Shibi, P. Manikandan, Misiriya Shahul Hameed & R. Arockia Xavier Annie

Authors
  1. T. Pradeep
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. R. Sai Shibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Manikandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Misiriya Shahul Hameed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. Arockia Xavier Annie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Misiriya Shahul Hameed .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, India

    A. Pasumpon Pandian

  2. Department of Engineering, University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

  4. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pradeep, T., Sai Shibi, M.R., Manikandan, P., Hameed, M.S., Arockia Xavier Annie, R. (2020). Summarization of User Reviews on E-Commerce Websites Using Hidden Markov Model. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24643-3_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24642-6

  • Online ISBN: 978-3-030-24643-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation