Advertisement

SMI-Based Opinion Analysis of Cloud Services from Online Reviews

  • Emna Ben-Abdallah
  • Khouloud Boukadi
  • Mohamed Hammami
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Nowadays, online reviews have become one of the most useful sources upon which cloud users can rely on to construct their purchasing decisions. This widespread adoption of online reviews results in nourishing the online opinion-based information. Analyzing and studying this kind of information help considerably in the fastidious task of cloud user decision-making. The contribution of this volume can be divided into two major parts: (i) a new opinion mining based cloud service analysis approach for the cloud service selection purpose. The proposed approach extracts and classifies user opinions from online reviews according to each cloud service property and (ii) an Opinion based cloud service ontology to effectively detect cloud service properties from reviews based on Service Measurement Index (SMI) metrics. To illustrate the proposed approach, we develop some experiments and we present the relevant results.

Keywords

Cloud computing Opinion mining Social analytic SMI Service property Ontology 

References

  1. 1.
    Han, A., Hao, L., Jifan, R.: An empirical study on inline impact factors of reviews usefulness based on movie reviews. In: 2016 13th International Conference on Service Systems and Service Management (ICSSSM), pp. 1–5, June 2016Google Scholar
  2. 2.
    Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data 2(1), 5 (2015)CrossRefGoogle Scholar
  3. 3.
    Cloud Service Measurement Index Consortium (CSMIC): SMI Framework. http://beta-www.cloudcommons.com/servicemeasurementindex
  4. 4.
    Ye, Q., Law, R., Gu, B.: The impact of online user reviews on hotel room sales. Int. J. Hosp. Manag. 28(1), 180–182 (2009)CrossRefGoogle Scholar
  5. 5.
    Chaudhari, D.D., Deshmukh, R.A., Bagwan, A.B., Deshmukh, P.K.: Feature based approach for review mining using appraisal words. In: 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications (C2SPCA), pp. 1–5, October 2013Google Scholar
  6. 6.
    Alkalbani, A.M., Gadhvi, L., Patel, B., Hussain, F.K., Ghamry, A.M., Hussain, O.K.: Analysing cloud services reviews using opining mining. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), pp. 1124–1129, March 2017Google Scholar
  7. 7.
    Jeong, H., Shin, D., Choi, J.: FEROM: feature extraction and refinement for opinion mining. ETRI J. 33, 720–730 (2011)CrossRefGoogle Scholar
  8. 8.
    Ali, F., Kwak, K.S., Kim, Y.G.: Opinion mining based on fuzzy domain ontology and support vector machine: a proposal to automate online review classification. Appl. Soft Comput. 47, 235–250 (2016)CrossRefGoogle Scholar
  9. 9.
    Rekik, M., Boukadi, K., Ben-Abdallah, H.: Cloud description ontology for service discovery and selection. In: Proceedings of the 10th International Conference on Software Engineering and Applications ICSOFT-EA, vol. 1, pp. 26–36 (2015)Google Scholar
  10. 10.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Amp; Posters. WWW Alt. 2004. ACM, New York, pp. 74–83 (2004)Google Scholar
  11. 11.
    Cunningham, H.: GATE, a general architecture for text engineering. Comput. Humanit. 36(2), 223–254 (2002)CrossRefGoogle Scholar
  12. 12.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Emna Ben-Abdallah
    • 1
  • Khouloud Boukadi
    • 1
  • Mohamed Hammami
    • 1
  1. 1.Mir@cl LaboratorySfax UniversitySfaxTunisia

Personalised recommendations