Efficient Recommendation System based on Keyword for SmartCity with Sentiment Analysis using Hadoop

Efficient Recommendation System based on Keyword for Smart-City with Sentiment Analysis using Hadoop

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-48 Number-1
Year of Publication : 2017
Authors : Ms. Sneha G. Potdar, Prof. N. S. More
DOI :  10.14445/22315381/IJETT-V48P204

Citation 

Ms. Sneha G. Potdar, Prof. N. S. More "Efficient Recommendation System based on Keyword for Smart-City with Sentiment Analysis using Hadoop", International Journal of Engineering Trends and Technology (IJETT), V48(1),17-23 June 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
In today’s life recommender system plays the important role while user is on social networking site, online shopping website. For presenting the personalized recommendation according to the new user’s demand is big challenge. The previous recommender system produces the recommendation without checking the personalized interest of user, due to that they couldn’t meet their proper requirement. In this paper, the recommender system works very efficiently using Hadoop by computing the reviews based on removing stop-words, stemming algorithm and sentiment analysis algorithm. Therefore, in the Smart-City application, the user getting accurate recommendation by computing reviews in positive, negative and neutral way according to new user’s keywords or sentences with the related services. Therefore, this paper approaches speed-up the performance over large amount of dataset using Map-Reduce. The proposed system puts forward an idea of aspect parsing technique and sentiment analysis technique which is forwarded with precision and recall measurement for an effective and accurate recommendation over reviews.

 References

[1] Shunmei Meng, Wanchun Dou, Xuyun Zhang, and Jinjun Chen, Senior Member, IEEE “KASR:A Keyword-AwareService Recommendation Method on MapReduce for Big Data Applications”, IEEE Transactions on Parallel and Distributed Systems, VOL. 25, NO. 12, DECEMBER 2014.
[2] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Item Based Collaborative Filtering Recommendation Algorithms” ACM, 10, May 15, 2001.
[3] Y. Chen, A. Cheng and W. Hsu, “Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos”. IEEE Transactions on Multimedia, Vol. 25, No.6, pp. 1283-1295, 2012.
[4] Xin Cao, Lisi Chen, Gao Cong, Jihong Guan, Nhan-Tue Phan, Xiaokui Xiao, “KORS: Keyword-aware Optimal Route Search System”, ICDE Conference, pages 1340-1343,IEEE 2013.
[5] J. Amaithi Singam and S. Srinivasan, “Optimal Keyword Search For Recommender System In Big Data Application”, ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 7, APRIL 2015.
[6] T.N. Chiranjeevi, R.H. Vishwanath , “PRS: personnel recommendation system for huge data analysis using porter stemmer ”, ICTACT JOURNAL ON SOFT COMPUTING, VOLUME: 06, ISSUE: 03,April 2016.
[7] X. Yang, Y. Guo, Y. Liu, “Bayesian-inference based recommendation in online social networks”, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 4, pp. 642-651, 2013.
[8] Xiaoyuan Su and Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques” Hindawi Publishing Corporation, Advances in Artificial Intelligence, Article ID 421425, 19 pages doi:10.1155/2009/421425, 2009.
[9] Yamini Nikam, M. B. Vaidya,“Survey On Service Recommendations Techniques” IJARIIE, Vol-2, Issue-1,2016.
[10] Khushboo R. Shrote, Prof A.V.Deorankar, “A Survey on Sentiment Analysis Based Keyword Aware Service Recommendation for Big Data”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 10, October 2015.
[11] Mr. T. Rajasekaran, P.Barathkumar, M.Guruprasath, S.Santhosh, “Keyword Conscious Provision Endorsement Method ”, IJLTEMAS, Volume IV, Issue III, March 2015.
[12] Khushboo R. Shrote, Prof A.V.Deorankar, “Sentiment Analysis Based Hotel Recommendation System”, Paripex - Indian Journal Of Research, Vol. 5, Issue 4 , April 2016.
[13] Rita Guimaraes, Demóstenes Z. Rodriguez, Renata L. Rosa, Graça Bressan, “Recommendation System using Sentiment Analysis considering the Polarity of the Adverb”, IEEE International Symposium on Consumer Electronics, 2016.
[14] Kazuyoshi Yoshii, Student Member, IEEE, Masataka Goto, Kazunori Komatani, Tetsuya Ogata, Member, IEEE, and Hiroshi G. Okuno, Senior Member, IEEE, “An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model”, IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 16, NO. 2, FEBRUARY 2008.
[15] Faustino Sanchez, María Alduan, Federico Alvarez, Member, IEEE, Jose Manuel Menendez, Member, IEEE, and Orlando Baez, “Recommender System for Sport Videos Based on User Audiovisual Consumption”, IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 14, NO. 6, DECEMBER 2012.
[16] Dr. M. Durairaj, K. Muthu Kumar, “New Recommendation Systems Using Web Mining:A Study”, IJETT, Vol 12, No. 6, Jun 2014.

Keywords
Accuracy, Efficient, Keyword, Recommender System, Smart-City, Sentiment Analysis, Hadoop, Map-Reduce.