Cooking Recipe Rating Based on Sentiment Analysis

Authors

  • B. Sandhiya Assistant professor, Sri Shakthi Institute of Engineering and Technology Author
  • Sujetha.B Sri Shakthi Institute of Engineering and Technology Author
  • Shneka. R Sri Shakthi Institute of Engineering and Technology Author

DOI:

https://doi.org/10.61841/5mq5e311

Keywords:

Food recipes, Sentiment analysis, Text analytics, Comment analysis

Abstract

Sentiment analysis of feedback on food recipe is to classify user responses to the positive or negative feedback on the food recipes. The suggested approach is appropriate by counting the polarity words on the food domain for evaluating feedback or opinions about food recipes. The aim of this research is to help users select the preferred recipes on online food commution from various food recipes. The program will rate the recipe, based on visitor feedback. So, it made finding the correct recipe simpler for people. With several people searching with online recipes this program would be helpful.Recipes you read obviously won't be the same as what you find after training. There are a number of inaccurate recipes you'll find online. Recipes must be rated by the user in order to cause the correct peoples. Here we propose a program that allows users to pick categories and post the recipes. Recipes are scored by the guests and commented on. So user will finish by finding a correct recipe.

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References

[1] G. Wang, S. Xie, B. Liu, and P. S. Yu, “Identify Online Store Review Spammers via Social Review Graph,” ACM Transactions on Intelligent Systems and Technology, Vol. 3, No. 4, pp.61:161:21, 2012.

[2] P. Pugsee, T. Chongvisuit and K. Na Nakorn, “Subjectivity Analysis for Airline Services from Twitter,” Proceeding of 2014 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 944947, 2014.

[3] X. Mao, Y. Rao, and Q. Li, “Recipe popularity prediction based on the analysis of social reviews,” Proceeding of the 2013 International Joint Conference on Awareness Science and Technology and Ubi-Media Computing (iCASTUMEDIA), pp. 568-573, 2013.

[4] B. Liu, and L. Zhang, A Survey of Opinion Mining and Sentiment Analysis, Mining Text Data, (editors: C. C. Aggarwal, and C. Zhai), Springer US, 2012.

[5] P. Pugsee, T. Chongvisuit and K. Na Nakorn, “Opinion mining on Twitter data for airline services,” Proceeding of the 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering (WCSE), pp. 639-644, 2015.

[6] C. B. Ward, Y. Choi, S. Skiena, and E. C. Xavier, “Empath: A Framework for Evaluating Entity-level

Sentiment Analysis,” Proceeding of the 8th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT), 2011.

[7] B. Liu, Sentiment Analysis and Subjectivity, Handbook of Natural Language Processing, 2nd ed. (editors: N. Indurkhya, and F. J. Damerau), Chapman & Hall/CRC Press, Taylor & Franics Group, 2010.

[8] L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Target-dependent twitter sentiment classification,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 151-160, 2011.

[9] M. Karamibekr, and A.A. Ghorbani, “Sentiment Analysis of Social Issues,” Proceeding of the 2012 International Conference on Social Informatics, pp. 215 - 221, 2012.

[10] B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd ed., Springer, July 2011.

[11] Y. Dang, Y. Zhang, and H. Chen, “A LexiconEnhanced Method for Sentiment Classification: An Experiment on Online Product Reviews,” IEEE Transactions on Intelligent Systems and Their Applications, Vol. 25, No. 4, pp. 46-53, 2010.

[12] S. Siersdorfer, S. Chelaru, J. S. Penro, I. S. Altingovde, and W. Nejdl, “Analyzing and Mining Comments and Comment Ratings on the Social Web,” ACM Transactions on the Web, Vol. 5, No. 10, pp.17:1-17:39, 2014.

[13] E. Momeni, K. Tao, B. Haslhofer, and GJ. Houben, “Identification of Useful User Comments in Social Media: A Case Study on Flickr Commons,” Proceeding of the 13th ACM/IEEECS Joint Conference on Digital Libraries, pp. 110, 2013.

[14] P. Pugsee, and M. Niyomvanich, “Comment Analysis for Food Recipe Preferences,” Proceeding of the 12th International Conference in Electrical Engineering/Electronics, Computer, Telecommunications (ECTICON), 2015.

[15] S. Kiritchenko, X. Zhu, and S. M. Mohammad, “Sentiment Analysis of Short Informal Texts,” Journal of Artificial Intelligence Research, Vol. 50, pp.723-762, 2014.

[16] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis,” Computational Linguistics, Vol. 35, No. 3, pp. 399-433, 2009.

[17] A. Esuli, and F. Sebastiani, “SENTIWORDNET: A publicly available lexical resource for opinion mining,” Proceeding of the 5th International Conference on Language Resources and Evaluation (LREC), pp. 417422, 2006.

[18] I. G. Councill, R. McDonald, and L. Velikovich, “What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis,” Proceedings of the Workshop on Negation and Speculation in Natural Language Processing (NeSp-NLP’10), pp. 51-59, 2010.

[19] P. Pugsee, and M. Niyomvanich, “Suggestion Analysis for Food Recipe Improvement,” Proceeding of the 2015 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA), 2015.

[20] C. Fellbaum, WordNet: an electronic lexical database, Cambridge, MA: MIT Press, 1998.

[21] L. Anthon. AntConc: A Freeware Corpus Analysis Toolkit for Concordancing and Text Analysis,

URL:http://www.laurenceanthony.Net/ software.html[Online].

[22] Lexalytics,Semantria,URL:https://semantria.com/[Online]

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Published

29.05.2020

How to Cite

B. Sandhiya, Sujetha.B, & Shneka. R. (2020). Cooking Recipe Rating Based on Sentiment Analysis . International Journal of Psychosocial Rehabilitation, 24(10), 1168-1187. https://doi.org/10.61841/5mq5e311