A Fine-Grained Ontology-Based Sentiment Aggregation Approach

  • Monireh Alsadat MirtalaieEmail author
  • Omar Khadeer Hussain
  • Elizabeth Chang
  • Farookh Khadeer Hussain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)


Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.


  1. 1.
    Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28, 813–830 (2016)CrossRefGoogle Scholar
  2. 2.
    Mirtalaie, M.A., Hussain, O.K., Chang, E., Hussain, F.K.: Sentiment analysis of specific product’s features using product tree for application in new product development. In: Advances in Intelligent Networking and Collaborative Systems (INCoS 2017), pp. 82–95. Springer, Cham (2017)Google Scholar
  3. 3.
    Carenini, G., Ng, R.T., Zwart, E.: Extracting knowledge from evaluative text. In: Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP 2005, pp. 11–18 (2005)Google Scholar
  4. 4.
    Ganesan, K., Zhai, C., Viegas, E.: Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In: International Conference on World Wide Web, Lyon, France, pp. 869–878 (2012)Google Scholar
  5. 5.
    Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of twitter posts. Expert Syst. Appl. 40, 4065–4074 (2013)CrossRefGoogle Scholar
  6. 6.
    Lau, R.Y.K., Li, C., Liao, S.S.Y.: Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis. Decis. Support Syst. 65, 80–94 (2014)CrossRefGoogle Scholar
  7. 7.
    Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, M.Á., Moreno, V., Fraga, A., Sánchez-Cervantes, J.L.: Feature-based opinion mining through ontologies. Expert Syst. Appl. 41, 5995–6008 (2014)CrossRefGoogle Scholar
  8. 8.
    Agarwal, B., Mittal, N., Bansal, P., Garg, S.: Sentiment analysis using common-sense and context information. Comput. Intell. Neurosci. 2015, 9 (2015). Article ID 715730 CrossRefGoogle Scholar
  9. 9.
    Mukherjee, S., Joshi, S.: Sentiment aggregation using ConceptNet ontology. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 570–578 (2013)Google Scholar
  10. 10.
    Basiri, M.E., Naghsh-Nilchi, A.R., Ghasem-Aghaee, N.: Sentiment prediction based on Dempster-Shafer theory of evidence. Math. Probl. Eng. 2014, 1–13 (2014)CrossRefGoogle Scholar
  11. 11.
    Behdenna, S., Barigou, F., Belalem, G.: Sentiment analysis at document level. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds.) Smart Trends in Information Technology and Computer Communications, pp. 159–168. Springer, Singapore (2016)CrossRefGoogle Scholar
  12. 12.
    Anto, M.P., Antony, M., Muhsina, K.M., Johny, N., James, V., Wilson, A.: Product rating using sentiment analysis. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3458–3462 (2016)Google Scholar
  13. 13.
    Mirtalaie, M.A., Hussain, O.K., Chang, E., Hussain, F.K.: A decision support framework for identifying novel ideas in new product development from cross-domain analysis. Inf. Syst. 69, 59–80 (2017)CrossRefGoogle Scholar
  14. 14.
    Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of ACL 2008: HLT, vol. 51, pp. 308–316 (2008)Google Scholar
  15. 15.
    Ye, K., Li, L., Guo, M., Qian, Y., Yuan, H.: Summarizing product aspects from massive online review with word representation. Knowl. Sci. Eng. Manag. 9403, 318–323 (2015)CrossRefGoogle Scholar
  16. 16.
    Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., Lee, C.: RevMiner: an extractive interface for navigating reviews on a smartphone. In: Proceedings of the 25th Annual ACM Symposium User Interface Software Technology, pp. 3–12 (2012)Google Scholar
  17. 17.
    Zhai, Z., Liu, B., Xu, H., Jia, P.: Grouping product features using semi-supervised learning with soft-constraints. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp. 1272–1280 (2010)Google Scholar
  18. 18.
    Kang, Y., Zhou, L.: RubE: rule-based methods for extracting product features from online consumer reviews. Inf. Manag. 54, 166–176 (2016)CrossRefGoogle Scholar
  19. 19.
    Samha, A.K., Li, Y., Zhang, J.: Aspect - based opinion mining from product reviews using conditional random fields. In: AusDM 2015: The 13th Australasian Data Mining Conference. University of Technology, Sydney (2015)Google Scholar
  20. 20.
    Mubarok, M.S., Adiwijaya, Aldhi, M.D.: Aspect-based sentiment analysis to review products using Naïve Bayes. In: AIP Conference Proceedings, vol. 1867, id 20060 (2017)Google Scholar
  21. 21.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  22. 22.
    Bae, J.K., Kim, J.: Product development with data mining techniques: a case on design of digital camera. Expert Syst. Appl. 38, 9274–9280 (2011)CrossRefGoogle Scholar
  23. 23.
    Hatcher, A.G.: An introduction to the analysis of English noun compounds. Word 16, 356–373 (1960)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Monireh Alsadat Mirtalaie
    • 1
    Email author
  • Omar Khadeer Hussain
    • 1
  • Elizabeth Chang
    • 1
  • Farookh Khadeer Hussain
    • 2
  1. 1.University of New South WalesCanberraAustralia
  2. 2.School of SoftwareUniversity of TechnologySydneyAustralia

Personalised recommendations