Skip to main content
SpringerLink
Log in

Polarized Extractive Summarization of Online Product Reviews

  • Conference paper
  • First Online:
Mining Intelligence and Knowledge Exploration (MIKE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13119))

  • 208 Accesses

Abstract

In the e-commerce domain, online customer reviews offer valuable information to manufacturers and potential buyers. Many fast-moving products receive hundreds or even thousands of online reviews. Therefore, summarizing multiple online reviews makes it easier for merchants and buyers to establish the usefulness of products and to prioritize the issues that are most important to them. Motivated by this, we designed an automated two-step pipeline for Polarized Extractive Summarization that uses Machine Learning and Deep Learning models. The first module of the pipeline is trained on a manually annotated Amazon cellphone dataset that contains multiple reviews of verified purchases on Amazon. Polarized Sentiments from these reviews are extracted by splitting the review(s) into sentences. The second module removes the duplicates and near-duplicates; the selected sentences are given to the graph-based model so as to rank these sentences based on the Link Analysis. The top-ranked sentences are chosen to generate positively and negatively polarized summaries. We evaluate our proposed models on manually annotated Amazon review dataset for sentence classification as well as summarization which yields better results on both modules. This research sets out to identify the sentiments in each review that contributes towards the summarization and removal of near duplicates. It has been shown from the experimental results that the generated summaries are more accurate and informative.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

Notes

  1. 1.

    Dataset: https://github.com/manju1201/Polarized-Summarization-of-Reviews.

  2. 2.

    Pre-trained GloVe embeddings: https://nlp.stanford.edu/projects/glove/.

  3. 3.

    Networkx Library: https://github.com/networkx/networkx,.

References

  1. Abdi, A., Hasan, S., Shamsuddin, S.M., Idris, N., Piran, J.: A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion. Knowl.-Based Syst. 213, 106658 (2021)

    Article  Google Scholar 

  2. Abdi, A., Idris, N., Alguliev, R.M., Aliguliyev, R.M.: Automatic summarization assessment through a combination of semantic and syntactic information for intelligent educational systems. Inf. Process. Manage. 51(4), 340–358 (2015)

    Article  Google Scholar 

  3. Bafna, K., Toshniwal, D.: Feature based summarization of customers’ reviews of online products. Procedia Comput. Sci. 22, 142–151 (2013)

    Article  Google Scholar 

  4. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International Conference on World Wide Web, no. 7, pp. 107–117. WWW7, Elsevier Science Publishers B.V., NLD (1998)

    Google Scholar 

  5. El-Kassas, W.S., Salama, C.R., Rafea, A.A., Mohamed, H.K.: Automatic text summarization: a comprehensive survey. Expert Syst. Appl. 165, 113679 (2021)

    Article  Google Scholar 

  6. Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data 2(1), 1–14 (2015)

    Article  Google Scholar 

  7. Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1–66 (2017)

    Article  Google Scholar 

  8. Goldstein, J., Mittal, V.O., Carbonell, J.G., Kantrowitz, M.: Multi-document summarization by sentence extraction. In: NAACL-ANLP 2000 Workshop: Automatic Summarization (2000)

    Google Scholar 

  9. Gong, Y., Liu, X.: Generic text summarization using relevance measure and latent semantic analysis. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19–25 (2001)

    Google Scholar 

  10. Gupta, V., Lehal, G.S.: A survey of text summarization extractive techniques. J. Emer. Technol. Web Intell. 2(3), 258–268 (2010)

    Google Scholar 

  11. Heu, J.U., Qasim, I., Lee, D.H.: Fodosu: multi-document summarization exploiting semantic analysis based on social folksonomy. Inf. Process. Manage. 51(1), 212–225 (2015)

    Article  Google Scholar 

  12. Hong, M., Wang, H.: Research on customer opinion summarization using topic mining and deep neural network. Math. Comput. Simul. 185, 88–114 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hou, T., Yannou, B., Leroy, Y., Poirson, E.: Mining customer product reviews for product development: a summarization process. Exp. Syst. Appl. 132, 141–150 (2019)

    Article  Google Scholar 

  14. Kar, M., Nunes, S., Ribeiro, C.: Summarization of changes in dynamic text collections using latent dirichlet allocation model. Inf. Process. Manag. 51(6), 809–833 (2015)

    Article  Google Scholar 

  15. Khan, A., Salim, N.: A review on abstractive summarization methods. J. Theoret. Appl. Inf. Technol. 59(1), 64–72 (2014)

    Google Scholar 

  16. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha (2014). https://doi.org/10.3115/v1/D14-1181

  17. Li, X., Wu, P., Zou, C., Xie, H., Wang, F.L.: Sentiment lossless summarization. Knowl.-Based Syst. 227, 107170 (2021)

    Google Scholar 

  18. Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: Coling 2008: Proceedings of the Workshop Multi-source Multilingual Information Extraction and Summarization, pp. 17–24 (2008)

    Google Scholar 

  19. Ly, D.K., Sugiyama, K., Lin, Z., Kan, M.Y.: Product review summarization from a deeper perspective. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 311–314 (2011)

    Google Scholar 

  20. Mallick, C., Das, A.K., Dutta, M., Das, A.K., Sarkar, A.: Graph-based text summarization using modified TextRank. In: Nayak, J., Abraham, A., Krishna, B.M., Chandra Sekhar, G.T., Das, A.K. (eds.) Soft Computing in Data Analytics. AISC, vol. 758, pp. 137–146. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0514-6_14

    Chapter  Google Scholar 

  21. Mani, I., Maybury, M.T.: Automatic summarization. In: ACL, 39th Annual Meeting and 10th Conference of the European Chapter, Companion Volume to the Proceedings of the Conference: Proceedings of the Student Research Workshop and Tutorial Abstracts, p. 5, 9–11 July 2001, Toulouse, France. CNRS, Toulose, France (2001)

    Google Scholar 

  22. Mehta, P.: Survey on movie rating and review summarization in mobile environment. Int. J. Eng. Res. Technol. 2(3) (2013)

    Google Scholar 

  23. Mihalcea, R.: Graph-based ranking algorithms for sentence extraction, applied to text summarization. In: Proceedings of the ACL Interactive Poster and Demonstration Sessions, pp. 170–173 (2004)

    Google Scholar 

  24. Miller, D.: Leveraging BERT for extractive text summarization on lectures. arXiv preprint arXiv:1906.04165 (2019)

  25. Moratanch, N., Chitrakala, S.: A survey on extractive text summarization. In: 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1–6. IEEE (2017)

    Google Scholar 

  26. Nazari, N., Mahdavi, M.: A survey on automatic text summarization. J. AI Data Mining 7(1), 121–135 (2019)

    Google Scholar 

  27. Sankarasubramaniam, Y., Ramanathan, K., Ghosh, S.: Text summarization using Wikipedia. Inf. Process. Manage. 50(3), 443–461 (2014)

    Article  Google Scholar 

  28. Tang, J., Yao, L., Chen, D.: Multi-topic based query-oriented summarization. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 1148–1159. SIAM (2009)

    Google Scholar 

  29. Wang, W.M., Li, Z., Tian, Z., Wang, J., Cheng, M.: Extracting and summarizing affective features and responses from online product descriptions and reviews: a kansei text mining approach. Eng. Appl. Artif. Intell. 73, 149–162 (2018)

    Article  Google Scholar 

  30. Wong, K.F., Wu, M., Li, W.: Extractive summarization using supervised and semi-supervised learning. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 985–992 (2008)

    Google Scholar 

  31. Xu, X., Meng, T., Cheng, X.: Aspect-based extractive summarization of online reviews. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 968–975 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Sri City, 630 Gnan Marg, Chittoor, Sri City, 517 646, Andhra Pradesh, India

    Gendeti Manjju Shree Devy, Korupolu Saideepthi, Varakala Sowmya & Rajendra Prasath

Authors
  1. Gendeti Manjju Shree Devy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Korupolu Saideepthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Varakala Sowmya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajendra Prasath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Gendeti Manjju Shree Devy , Korupolu Saideepthi , Varakala Sowmya or Rajendra Prasath .

Editor information

Editors and Affiliations

  1. Université de Pau et des Pays de l’Adour, Anglet, France

    Richard Chbeir

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh, India

    Rajendra Prasath

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Devy, G.M.S., Saideepthi, K., Sowmya, V., Prasath, R. (2022). Polarized Extractive Summarization of Online Product Reviews. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21517-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21516-2

  • Online ISBN: 978-3-031-21517-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation