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Reseach Article

Application of Text Mining to Build a Recommendation System for Restaurants and their Dishes

by Rafey Anjum, Harsh Dev
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 4
Year of Publication: 2016
Authors: Rafey Anjum, Harsh Dev
10.5120/ijca2016912284

Rafey Anjum, Harsh Dev . Application of Text Mining to Build a Recommendation System for Restaurants and their Dishes. International Journal of Computer Applications. 155, 4 ( Dec 2016), 1-6. DOI=10.5120/ijca2016912284

@article{ 10.5120/ijca2016912284,
author = { Rafey Anjum, Harsh Dev },
title = { Application of Text Mining to Build a Recommendation System for Restaurants and their Dishes },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 4 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number4/26590-2016912284/ },
doi = { 10.5120/ijca2016912284 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:20.575767+05:30
%A Rafey Anjum
%A Harsh Dev
%T Application of Text Mining to Build a Recommendation System for Restaurants and their Dishes
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 4
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Blogs, comments and reviews have now become an integral part of people who want to read them in order to be informed regarding other people opinion. This helps them to gain an overview of what other people say so that they might take a decision based on other people recommendation. Most of the time the user may not been in a position to read all the opinions and then take an informed decision about the product or services which he/she wants to take. Also it has been seen that most of the websites use different approach like star rating, numerical rating, to depict the information to the people who want to read the reviews. In this paper our aim is to develop a system for providing a method to help and explore good restaurants and specific dishes which a user wants to know based on past experiences of the people. The basic approach is to extract opinions from the websites and to extract the meaning of those sentences by applying Natural Language Processing techniques and then give the rating on a 5-point scale.

References
  1. Hu, M. and Liu, B.: Mining and Summarizing Customer Reviews, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 168 – 177, USA (2004)
  2. Liu, B., Hu, M., and Cheng, J. Opinion Observer - Analyzing and comparing opinions on the Web, in: Proceedings of the 14th International Conference on World Wide Web (WWW’05), pp. 342-351, Japan (2005)
  3. Kim, S. and Hovy, E.: Determining the Sentiment of Opinions, in: Proceedings of the 20th International Conference on Computational Linguistics (COLING’04), pp. 1367-1373, Switzerland (2004)
  4. Popescu, A. M. and Etzioni, O.: Extracting Product Features and Opinions from Reviews, Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP’05), pp. 339 – 346, Canada (2005)
  5. 9. Pang, B. and Lee, L.: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, in: Proceedings of ACL’04, pp. 271-278, (2004)
  6. Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, in: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL’02), pp. 417 – 424, Philadelphia, Pennsylvania (2002)
  7. Pang, B., Lee, L. and Vaithyanathan, S.: Thumbs up? Sentiment Classification Using Machine Learning Techniques, in: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP’02), pp. 79 – 86, USA (2002)
  8. Gamgarn Somprasertsri and Pattarachai Lalitrojwong “Mining Feature-Opinion in Online CustomerReviews for Opinion Summarization”. Journal of Universal Computer Science, vol. 16, no. 6(2010),pp 938-955.
  9. Stanford typed dependencies manual.Marie-Catherine de Marneffe and Christopher D.Manning September 2008 Revised for Stanford Parser v.3.5.2 in April 2015
Index Terms

Computer Science
Information Sciences

Keywords

Natural language processing Text mining Recommendation system