Implementing E-Commerce Platform for Quality Evaluation Using Product Reviews

  IJCTT-book-cover
 
         
 
© 2021 by IJCTT Journal
Volume-69 Issue-4
Year of Publication : 2021
Authors : Gautami Tilve, Krutika Valanj, Aishwarya Bhor, Vaibhav Waghmare
DOI :  10.14445/22312803/IJCTT-V69I4P112

How to Cite?

Gautami Tilve, Krutika Valanj, Aishwarya Bhor, Vaibhav Waghmare, "Implementing E-Commerce Platform for Quality Evaluation Using Product Reviews," International Journal of Computer Trends and Technology, vol. 69, no. 4, pp. 66-73, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I4P112

Abstract
Electronic trade (e-commerce) is a paradigm that can influence both marketers and consumers. On other hand, E-commerce is considered to be an alternative to boost up the current marketing strategy. It guides the entire transformation of the conventional business model. This massive change in business model is exploding the globe, including India. Now days the E-commerce performs a vital role past 1.5 years due to COVID -19 pandemic as well as continue its growing journey for nextup coming years as well. For shopping or buying the stuff online Product Review on the product quantity service and the plays an important role. The product review elaborate the product quality, Service or delivery time and many more detail information about the product. To understand the person’s intent about the products sentiment analysis is always targeted as to Positive or negative respectively. In this paper an attempt is made to introduce the ECommerce Platform for Quality Evaluation Using Product Reviews. Here the survey by different researcher is elaborated to get an overall idea about different techniques that contribute the improvement of the E- commerce frame work now days. A full vision of the proposed work that we introduce that aids in determining whether facets of the product are positive, negative, or neutral using the aspect raking technique.

Keywords
E-Commerce Platform, Analysis of product Review, Aspects Ranking, Overall Product Ranking, positive or negative intent.

Reference

[1] Jongwook Woo, Dong-Yon Kim, Wonhong Cho, MinSeok Jang, Integrated Information Systems Architecture in e- Business, (June 2011).
[2] Gaurav Gupta and Himanshu Aggarwal, Improving Customer Relationship Management Using Data Mining, International Journal of Machine Learning and Computing, 2(6) (2012).
[3] J. D. Holt and S. M. Chung, Parallel Mining of Association Rules from Text Databases, The Journal of Supercomputing, 39(3) Springer, (2007) 273-299.
[4] Anna Gatzioura and Miquel Sanchez-Marre, Universitat Politecnicade Catalunya Barcelona Tech, A Case-Based Recommendation Approach for Market Basket Data, 2015 IEEE Intelligent Systems.
[5] RokhmatulInsani ,HiraLaksmiwatiSoemitro , Data Mining for Marketing in Telecommunication Industry, 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia.
[6] YalingXun, Jifu Zhang, Xiao Qin, Senior Member, IEEE, and XujunZhao, FiDoop-DP: Data Partitioning in Frequent Itemset Mining on HadoopClus- ters, IEEE Transactions on Parallel and Distributed Systems 2016.
[7] XIE Wen-xiu, QiHeng-nian, Huang Mei-li, Market basket analysis based ontext segmentation and association rule mining, 2010 First International Conference on Networking and Distributed Computing.
[8] Wan Faezah Abbas, Nor Diana Ahmad, Nurlina Binti Zaini, Discovering Purchasing Pattern of Sport Items Using Market Basket Analysis,2013 Interna- tional Conference on Advanced Computer Science Applications and Technologies.
[9] Golchia Jenabi1, Seyed Abolghasem Mirroshandel, Using Data Mining Techniques for Improving Customer Relationship Management, European Online Journal of Natural and Social Sciences 2013.
[10] Keyvan Vahidy Rodpysh1, Amir Aghai2andMeysam Majdi3, Applying Data Miningin Customerrelationship Management, International Journal of Information Technology, Control and Automation (IJITCA) Vol.2, No.3,July2012.
[11] D’Hean J., Poel, D.V &Thorleuchter, D. (2013). Predicting customer profitability acquisition: Finding the optimal combination of data source and data mining technique. Expert system with applications, 40, 2007-22012.
[12] JafariMomtaz, N., Alizadeh, S. & Sharif Vaghefi, M. (2010). A new model for assessment fast food customer behavior case study an Iranian fast food restaurant. British food journal, 115, 4, 601-613.
[13] Zheng-Jun Zha Jianxing Yu, Meng Wang Member, and Tat-Seng Chua, Product Aspect Ranking and Its Applications, A Submission To Ieee Transactions On Knowledge And Data Engineering1041- 4347/13/$31.00 © 2013 IEEE.