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Product Review Summarization for E-Commerce Site Using Gibbs Sampling Based LDA

Minakshi Ghorpade, Mrs. Megharani Patil,

Affiliations
Department of Computer Engineering, TCET Mumbai, India
:10.22362/ijcert/2019/v6/i01/v6i0102


Abstract
In E-commerce, the Reputation based trust models are extremely important for business growth. E-commerce website becomes more important in our day to days life because of varieties of information provided by it. 75 percent people are utilizing it for buying on the web items. The number of customer reviews on various products are increasing day-by-day. These vast numbers of reviews are beneficial to manufacturers and customers alike. It is a challenging task for a potential customer to read all reviews to make a better purchase decision. This system is a web-based application where user will view and purchase various products online, user can provide review about the products and online shopping services. The System takes review of various users and based on the review, system will specify whether the products and services provided by the E-commerce enterprise is good, bad or worst. The proposed work includes a multidimensional trust model for computing reputation scores from user`s reviews. To implement this a Modified LDA algorithm for mining dimensions of ecommerce feedback comments is used. In this proposed work natural language processing and opinion mining techniques are used. This paper also includes the comparison based on accuracy, time complexity, a brief introduction information world and touch topic likes trust score, reputation trust and their ratings using Gibbs-sampling that creates various categories for feedback and assigns trust score.


Citation
Minakshi Ghorpade, Mrs Megharani Patil."Product Review Summarization for E-Commerce Site Using Gibbs Sampling Based LDA". International Journal of Computer Engineering In Research Trends (IJCERT), ISSN: 2349-7084, Vol.6, Issue 01, January - 2019,


Keywords : E-commerce, SentiWordNet, NLP, Text mining, Modified LDA

References
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DOI Link : https://doi.org/10.22362/ijcert/2019/v6/i01/v6i0102

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