International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Subha Jyoti Dasa and Basabi Chakrabortyb*

aGraduate School of Software and Information Science, Iwate Prefectural University, Iwate, Japan
bFaculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan


Download Citation: |
Download PDF


Customer feedback in the form of online reviews is an important source of information to manufacturers or service providers for evaluation of their products or services. Online reviews also help potential buyers in making their decisions.  Manual checking of these huge amount of unstructured texts is time consuming. Several attempts have been made for opinion aggregation of online reviews but a generalized automated technique has yet to be developed. In this work, an efficient rule based technique for aspect wise summarization of online product reviews irrespective of their categories has been designed. The proposed technique develops the rules for extracting aspects and associating the opinion words to the respective aspects followed by effective grouping and summarization of aspect-opinion pairs into human interpretable form. The algorithm has been implemented on Amazon Product Reviews and evaluated against manually annotated ground truth. The result shows promising similarity with human judgement.

Keywords: Online review; aspect extraction; opinion aggregation; Word2Vec model.

Share this article with your colleagues



  1. [1] Mukherjee, A. and Liu, B. 2012. Modeling Review Comments. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL), 1: 320–329. 

  2. [2] Moussa, M. E., Mohamed, E. H. and Haggag M. H. 2018. A survey on opinion summarization techniques for social media. Future Computing and Informatics Journal, 3, 1: 82–109.[Publisher Site]

  3. [3] Bafna, K. and Toshniwal, D. 2013. Feature based Summarization of Customers Reviews of Online Products. 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems(KES2013), doi:10.1016/j.procs.2013.09.090[Publisher Site]

  4. [4] Fabbrizio, G. D., Aker, A. and Gaizauskas, R. 2013. Summarizing Online Reviews Using Aspect Rating Distributions and Language Modeling. IEEE Intelligent Systems, 28, 3: 28–37. [Publisher Site]

  5. [5] Mikolov, T., Sutskever, I, Chen, K., Corrado, G. and Dean, J. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS 2013, 3111–3119.

  6. [6] Rao, A. and Shah, K. 2018. A Domain Independent Technique to generate Feature Opinion Pairs for Opinion Mining. WSEAS Transactions on Information Science and Applications, 15, 7: 61–69.

  7. [7] Wawer, A. 2015. Towards Domain Independent Opinion Target Extraction. IEEE 15th International Conference on Data Mining Workshops (ICDMW), 1326–1331.  [Publisher Site]

  8. [8] Bagheri, A., Saraee, M. and Jong, F. d., 2013. Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52: 201–213. [Publisher Site]

  9. [9] Hai, Z., Chang, K., Kim, J. J. and Yang, C. C. 2014. Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance. IEEE Transactions on Knowledge and Data Engineering, 26, 3: 623–634. [Publisher Site]

  10. [10] Pany, S. J., Niz, X., Sunz, J. T.,Yangy, and Chen, Z., 2010. Cross-Domain Sentiment Classification via Spectral Feature Alignment, WWW ’10 Proceedings of the 19th international conference on World wide web Pages, Raleigh, North Carolina, USA doi:10.1145/1772690.1772767. [Publisher Site]

  11. [11] Khan, J. and Jeong, B. S. 2016. Summarizing customer review based on product feature and opinion. International Conference on Machine Learning and Cybernetics (ICMLC), IEEE. doi:10.1109 /ICMLC.2016.7860894. [Publisher Site]

  12. [12] Nyaung, D. E. and Thein, T. L. L. 2015. Feature Based Summarizing and Ranking from Customer Reviews. International Journal of Computer and Information Engineering, 9, 3: 734–739.

  13. [13] Hanni, A. R., Patil, M. M. and Patil, P. M. 2016. Summarization of Customer Reviews for a Product on a website using Natural Language Processing. International Conference on Advances in Computing, Communications and Informatics (ICACCI),IEEE, doi:10.1109/ICACCI.2016.7732392. [Publisher Site]

  14. [14] Zhang, R., Zhang Z., He, X. and Zhou, A. 2015. Dish Comment Summarization Based on Bilateral Topic Analysis. In Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE). 483494. doi:1109/ICDE.2015.7113308.  [Publisher Site]

  15. [15] Ester, M. and Moghaddam, S. 2010. Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews. In Proceedings of the 19th ACM international conference on Information and knowledge management. doi:10.1145/1871437.1871739.  [Publisher Site]

  16. [16] Mukherjee, A. and Liu, B. 2012. Aspect Extraction through Semi-Supervised Modeling. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL), 1: 339–348.

  17. [17] Liu, C. L., Hsaio, W. H., Lee, C. H., Lu, G. C., and Jou, E. 2012. Movie Rating and Review Summarization in Mobile Environment. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42, 3: 397–407. [Publisher Site]

  18. [18] Tsaparas, P., Ntoulas, A. and Terzi, E. 2011. Selecting a comprehensive Set of Reviews. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 3: 168–176. [Publisher Site]

  19. [19] Hu, H. W., Chen, Y. L. and Hsu, P. T. 2016. A Novel Approach To Rate and Summarize Online Reviews According to User-Specified Aspects. Journal of Electronic Commerce Research, 17, 2: 132–152.

  20. [20] Bhatia, N., and Jaiswal, A. 2016. Automatic text summarization and its methods- a review. 6th International Conference Cloud System and Big data Engineering (Confluence), Noida, 65–72. [Publisher Site]

  21. [21] Ruining He, Jullian McAuley, 2016, Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. World Wide Web conference. [Publisher Site]


Received: 2019-05-28
Revised: 2019-12-23
Accepted: 2019-03-29
Available Online: 2020-06-01

Cite this article:

Das, S.J., Chakraborty, B. 2020. Design of a category independent, aspect based automated opinion analysis technique for online product reviews. International Journal of Applied Science and Engineering, 17, 175–189.

We use cookies on this website to improve your user experience. By using this site you agree to its use of cookies.