Cluster Computing

, Volume 22, Supplement 2, pp 3175–3181 | Cite as

A novel approach for ranking customer reviews using a modified PSO-based aspect ranking algorithm

  • Osama AlfarrajEmail author
  • Ahmad Ali AlZubi


Buyer suggestions are helped customers to consider the qualities and shortcomings of various items and find resources that best suit their requirements. However, the suggestions are a challenge for different organizations, because of the veracity, velocity, variety, and volume veracity. Consequently, the customers looks at the indicators of readership and support of client audits, utilizing a sentiment mining approach for big data analytics. Consumer reviews for products are submitted on the web. Since the criticisms are improper it is difficult to gather all the information. This work recommends a new method particle swarm optimization for investigate the aspect-sentiment analysis. The objective of this method is to acquire a rundown of the best and the most undesirable attributes of a specific item, given an accumulation of free-text client audits. This method begins by coordinating user suggestions carefully assembled in independent sentences to discover assessments communicated towards hopeful aspects. We implement a probabilistic ranking aspect method to deduce the significant aspects while understanding the impact to common purchaser sentiments and viewpoints. The experimental results show that the proposed algorithm gives the best result compared to the existing work.


Big data PSO Aspect ranking Data mining Sentiment mining approach 



This project was supported by King Saud University, Deanship of Scientific Research, Community College Research Unit.


  1. 1.
    Miss Dhanashri Rohidas Londhe: Product aspect ranking on the consumers reviews. Int. J. Adv. Res. Innov. Ideas Educ.-ISSN(O) 2(2) (2016)Google Scholar
  2. 2.
    Tikait, R., Badre, P.R., Kinikar, P.M.: Product aspect identification and ranking system. Int. J. Sci. Eng. Technol. Res. 4(4), 1127–1131 (2015)Google Scholar
  3. 3.
    Miss. Dhanashri Rohidas Londhe: Product aspect ranking on the consumers reviews and its applications. Int. J. Adv. Res. Comput. Commun. Eng. 5(7), 273–280 (2016)Google Scholar
  4. 4.
    Zha, Z.J., Yu, J., Tang, J., Wang, M., Chua, T.S.: Product aspect ranking and its applications. IEEE Trans. Knowl. Data Eng. 26, 1211–1224 (2013)Google Scholar
  5. 5.
    Meenakshi, M., Sindhu, D.: An identifying impartment of product using aspect ranking. Int. J. Sci. Res. Manag. 3(4), 2628–2631 (2015)Google Scholar
  6. 6.
    Sai Krishna, P., Geethalatha, M.: An efficient method on identification of product aspect and ranking system. Int. J. Sci. Res. 4(12), 1727–1730 (2015)Google Scholar
  7. 7.
    Ancy, J.S., Nisha, J.R.: A survey on product aspect ranking techniques. Int. J. Innov. Res. Comput. Commun. Eng. 3(4), 14 (2015)Google Scholar
  8. 8.
    Vaitheeswaran, G., Arockiam, L.: A novel lexicon based approach to enhance the accuracy of sentiment analysis on big data. Int. J. Emerg. Res. Manag. Technol. 5(1), 12 (2016)Google Scholar
  9. 9.
    Gupta, D.K., Reddy, K.S., Ekbal, A.: Pso-asent: feature selection using particle swarm optimization for aspect based sentiment analysis. In: International Conference on Applications of Natural Language to Information Systems, vol. 9103, pp. 220–233. Springer, Cham (2015)Google Scholar
  10. 10.
    S. Bharathikannamma, R. Hanitha, H. Manochitra, D. Loganayaki, M.E.: Product aspect ranking using probabilistic aspect ranking algorithm. Int. J. Innov. Trends Emerg. Technol. 1(2), ISSN 23499842 (Online), 15 (2015)Google Scholar
  11. 11.
    Lokhande, D., Rohini, K., Pooja, M.: Aspect extraction ranking of product for online reviews. Int. J. Comput. Appl. (0975–8887) (2015)Google Scholar
  12. 12.
    Yu, J., Zha, Z.J., Wang, M., Chua, T.S.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1496–1505 (2011)Google Scholar
  13. 13.
    Selvakumar, S., HannahInbarani, H.: Covering rough set based intelligent clustering approach for social e-learning systems. Int. J. Appl. Eng. Res. 10(20), 19505–19510 (2015)Google Scholar
  14. 14.
    Selvakumar, S., HannahInbarani, H.: Rough set–based meta–heuristic clustering approach for social e–learning systems. Int. J. Intell. Eng. Inf. 3(1), 23–41 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science Department, Community CollegeKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations