Enhance Rating Algorithm for Restaurants

  • Jeshreen BalrajEmail author
  • Cassim Farook
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


More and more people make their purchase decisions by referring to reviews and ratings provided in online platforms. Visitors to restaurants use online reviews on a larger scale compared with the users of other industries. However, for these visitors, evaluating numerous reviews is a hassle and time consuming as it involves a process of reading through all the reviews, identifying the date of review posting, understanding the reviewer’s credibility and identifying the rating of the reviewer and the restaurant before making the decision. This research proposes an enhanced rating algorithm which will calculate an overall rating. Apart from the standard point rating the solution will include the aspect, sentiment, time factor and user credibility of a review. The enhanced algorithm uses Natural Language Processing and Sentiment analysis used with machine learning to identify the thoughts of the user regarding the restaurants. The algorithm is tested with a web-based solution that gives an overall idea of the current performance of a particular restaurant utilizing reviews of those restaurants. The new algorithm gives a much credible rating than the conventional rating systems.


Natural language processing Sentiment analysis Algorithm Machine learning 


  1. 1.
    BrightLocal, “Local Consumer Review Survey 2016 | The Impact Of Online Reviews,” 2017. (Online). Available: Accessed 05 Sep 2017
  2. 2.
    Dohse, K.A.: Fabrication feedback: blurring the line between brand management and bogus reviews. J. Law Technol. Policy 1, 363–392 (2013)Google Scholar
  3. 3.
    Lee, I., Sun, Y., Li, Y.S.: An Intelligent Approach to Review Filtering and Review Quality Improvement, pp. 61–66 (2016)Google Scholar
  4. 4.
    J. Gobinath, J., Gupta, D.: Online reviews: determining the perceived quality of information. In: 2016 International Conference Advanced Computing Communication Informatics, pp. 412–416 (2016)Google Scholar
  5. 5.
    Bambauer-Sachse, S., Mangold, S.: Do consumers still believe what is said in online product reviews? A persuasion knowledge approach. J. Retail. Consum. Serv. 20(4), 373–381 (2013)CrossRefGoogle Scholar
  6. 6.
    Miller, E.: How Not To Sort By Average Rating—Evan Miller., 2009. (Online). Available: Accessed 11 Feb 2018
  7. 7.
    EBC.: How to Rank (Restaurants) | ebc,” 2015. (Online). Available: Accessed 05 Jul 2017
  8. 8.
    Rosairo Wenbert Del.: Getting the Bayesian Average for rankings (PHP/MySQL)| by Wenbert Del Rosario. EKINI, 2013. (Online). Available: Accessed 13 Feb 2018
  9. 9.
    University of Pennsylvania.: Penn Treebank P.O.S. Tags. 2003. (Online). Available: Accessed 05 Mar 2018
  10. 10.
    De Marneffe, M.-C., Manning, C.D.: Stanford typed dependencies manual (2008)Google Scholar
  11. 11.
    SentiWordNet.: SentiWordNet. 2010. (Online). Available: Accessed 05 Apr 2018
  12. 12.
    A. Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2018)Google Scholar
  13. 13.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)CrossRefGoogle Scholar
  14. 14.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up? Sentiment Classification using Machine Learning Techniques, pp. 79–86Google Scholar
  15. 15.
    Shetty, J.: Sentiment Analysis of Product Reviews no. Icicct, pp. 298–303 (2017)Google Scholar
  16. 16.
    Hassan, S., Rafi, M., Shaikh, M.S.: Comparing SVM and Naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment. In: Proceedings of 14th IEEE International Multitopic Conference 2011, INMIC 2011, pp. 31–34 (2011)Google Scholar
  17. 17.
    Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, (2008)Google Scholar
  18. 18.
    Xue, Y., Chen, H., Jin, C., Sun, Z., Yao, X.: NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm. BMC Bioinform. 7(1), 458 (2006)CrossRefGoogle Scholar
  19. 19.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python Gaël Varoquaux. J. Mach. Learn. Res. 12, 2825–2830 (2011)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Informatics Institute of TechnologyColomboSri Lanka

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