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Product Rating using Opinion Mining

Sunil B. Mane, Kruti Assar, Priyanka Sawant and Monika Shinde, ,

Affiliations
Assistant Professor, Department of Computer Engineering and Information Technology, College of Engineering Pune Pune - 411005, Maharashtra, India.

:10.22362/ijcert/2017/v4/i5/xxxx [UNDER PROCESS]

Abstract

Amazon.com is one of the largest electronic commerce website in the world which allows users to purchase different products and submit reviews on each one of them. The reviews allow the first-time buyers to understand the quality of the products and decide whether to make a purchase or not. The reviews result in unstructured big data which can be analyzed and used for recommendation of a product on the website. However, it is possible that some customers write fake reviews to promote or defame a particular brand. So it is important to detect and remove the fake reviews for providing the correct rating to the product. Also, it is necessary to create a fast and efficient system for analyzing big data. The present systems used for big data analysis are quite slow. So here, we use the Apache Spark framework for increasing the speed of processing the Amazon reviews. This paper provides a new implementation for analyzing Amazon reviews which involve detection of fake reviews, processing the genuine reviews using Apache Spark and finally rating the products.

Citation

Sunil B. Mane et.al, â€œProduct Rating using Opinion Miningâ€, International Journal of Computer Engineering In Research Trends, 4(5):161-168 ,May -2017.

Keywords : Opinion Mining, Apache Spark, Product Rating, Fake Review Detection, Natural Language Processing, Sentiment Analysis.

References

[1] N. Nodarakis, S. Sioutas, A. Tsakalidis, and G. Tzimas. Large-Scale Sentiment Analysis On Twitter with Spark. Mar 15, 2016.
[2] Enock Kanyesigye , Sumitra Menerea," Sentiment Analysis Of Reviews Using Hadoop". 2016.
[3] J. McAuley, R. Pandey, J. Leskovec Knowledge Discovery and Data Mining, 2015.
[4] J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015
[5] Eman M.G. Younis, Faculty of Computer and Information Minia University, Egypt, "Sentiment Analysis and Text Mining for Social Media Microblogs using Open Source Tools: An Empirical Study".February 2015
[6] Poobana S, Sashi Rekha k, "Opinion Mining From Text Reviews Using Machine Learning Algorithm ".3, March 2015
[7] Mrs. Uma Gurav, Prof. Dr. Nandini sidnal, "Opinion mining for reputation evaluation on unstructured Big Data " . 4, April 2015
[8] Spark. The apache software foundation: Spark homepage. http://spark.apache.org/, 2015. [Online; accessed 27-December-2015]
[9] Sunil B. Mane, Y. Sawant, S. Kazi, and V. ShindeReal.Time Sentiment Analysis of Twitter Data Using Hadoop,College of Engineering, Pune. 2014
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[12] Kalyankumar B Waddar, K Srinivasa, "OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP".Jul 5,2014
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[17] Bing Liu, Minquing hu, â€œMining and summarizing Customer Reviewsâ€. 2004
[18] K. Waddar and K. Srinivasa. OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP
[19] B. Pang, L. Lee, and S. Vaithyanathan. Sentiment classification using machine learning techniques.
[20] Maria Soledad Elli, Yi-Fan Wang, Amazon Reviews, business analytics with sentiment analysis

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Citation Index

Citations Indices	All
Citations	1026
h-index	14
i10-index	20
Source: Google Scholar

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