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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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
Dohse, K.A.: Fabrication feedback: blurring the line between brand management and bogus reviews. J. Law Technol. Policy 1, 363–392 (2013)
Lee, I., Sun, Y., Li, Y.S.: An Intelligent Approach to Review Filtering and Review Quality Improvement, pp. 61–66 (2016)
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)
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)
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
EBC.: How to Rank (Restaurants) | ebc,” 2015. (Online). Available: http://www.ebc.cat/2015/01/05/how-to-rank-restaurants/. Accessed 05 Jul 2017
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
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
De Marneffe, M.-C., Manning, C.D.: Stanford typed dependencies manual (2008)
SentiWordNet.: SentiWordNet. 2010. (Online). Available: http://sentiwordnet.isti.cnr.it/. Accessed 05 Apr 2018
A. Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2018)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up? Sentiment Classification using Machine Learning Techniques, pp. 79–86
Shetty, J.: Sentiment Analysis of Product Reviews no. Icicct, pp. 298–303 (2017)
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)
Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, (2008)
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)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python Gaël Varoquaux. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
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
DOI: https://doi.org/10.1007/978-3-030-12385-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12384-0
Online ISBN: 978-3-030-12385-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)