Back to Current Issues

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, “Product Rating using Opinion Mining”, 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
[10] Anju Gahlawat. Big Data Analysis using R and Hadoop. September 2014
[11] Pravesh Kumar Singh, Mohd Shahid Husain, "METHODOLOGICALSTUDY OFOPINION MINING AND SENTIMENT ANALYSIS TECHNIQUES".  February 2014
[12] Kalyankumar B Waddar, K Srinivasa, "OPINION MINING IN PRODUCT REVIEW SYSTEM USING BIG DATA TECHNOLOGY HADOOP".Jul 5,2014
[13] Julia Kreutzer And Neele Witte, Opinion Mining Using SentiWordNet, Semantic Analysis, Uppsala University. 2013/14
[14] Arjun Mukherjee, Vivek Venkataraman, Bing Liu, Natalie Glance, "Fake Review Detection: Classification and Analysis of Real and Pseudo Reviews" .2013
[15] Nitin Jindal and Bing.Opinion Spam and Analysis.Department of Computer Science, University of Illinois at Chicago.Feb 12, 2008
[16] Bo Pang, Lillian Lee, “Opinion Mining and Sentiment Analysis” . 2008
[17] Bing Liu, Minquing hu, “Mining and summarizing Customer Reviews”. 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



DOI Link : Not yet assigned

Download :
  V4I503.pdf


Refbacks : There are no refback

Quick Links


DOI:10.22362/ijcert


Science Central

Score: 13.30



Submit your paper to editorijcert@gmail.com

>