Skip to main content

Enhance Rating Algorithm for Restaurants

  • Conference paper
  • First Online:
  • 1496 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 70))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. BrightLocal, “Local Consumer Review Survey 2016 | The Impact Of Online Reviews,” 2017. (Online). Available: https://www.brightlocal.com/learn/local-consumer-review-survey/. Accessed 05 Sep 2017

  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. Lee, I., Sun, Y., Li, Y.S.: An Intelligent Approach to Review Filtering and Review Quality Improvement, pp. 61–66 (2016)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  6. Miller, E.: How Not To Sort By Average Rating—Evan Miller. Evanmiller.org, 2009. (Online). Available: https://www.evanmiller.org/how-not-to-sort-by-average-rating.html. Accessed 11 Feb 2018

  7. EBC.: How to Rank (Restaurants) | ebc,” 2015. (Online). Available: http://www.ebc.cat/2015/01/05/how-to-rank-restaurants/. Accessed 05 Jul 2017

  8. Rosairo Wenbert Del.: Getting the Bayesian Average for rankings (PHP/MySQL)|Ekini.net by Wenbert Del Rosario. EKINI, 2013. (Online). Available: http://blog.ekini.net/2013/08/18/getting-the-bayesian-average-for-rankings-mysql/. Accessed 13 Feb 2018

  9. University of Pennsylvania.: Penn Treebank P.O.S. Tags. 2003. (Online). Available: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html. Accessed 05 Mar 2018

  10. De Marneffe, M.-C., Manning, C.D.: Stanford typed dependencies manual (2008)

    Google Scholar 

  11. SentiWordNet.: SentiWordNet. 2010. (Online). Available: http://sentiwordnet.isti.cnr.it/. Accessed 05 Apr 2018

  12. A. Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2018)

    Google Scholar 

  13. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  14. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up? Sentiment Classification using Machine Learning Techniques, pp. 79–86

    Google Scholar 

  15. Shetty, J.: Sentiment Analysis of Product Reviews no. Icicct, pp. 298–303 (2017)

    Google Scholar 

  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. Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  19. Pedregosa, F., et al.: Scikit-learn: machine learning in Python Gaël Varoquaux. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeshreen Balraj .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Balraj, J., Farook, C. (2020). Enhance Rating Algorithm for Restaurants. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_18

Download citation

Publish with us

Policies and ethics