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Prediction of Online Products Rating Using Textual Review Social Sentiment

A. Sharma1 , Iyapparaja M.2

  1. School of Information Technology and Engineering, V. I. T. University, Vellore, India.
  2. School of Information Technology and Engineering, V. I. T. University, Vellore, India.

Correspondence should be addressed to: iyapparaja.m@vit.ac.in.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-5 , Page no. 162-169, May-2017

Online published on May 30, 2017

Copyright © A. Sharma, Iyapparaja M. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. Sharma, Iyapparaja M., “Prediction of Online Products Rating Using Textual Review Social Sentiment,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.162-169, 2017.

MLA Style Citation: A. Sharma, Iyapparaja M. "Prediction of Online Products Rating Using Textual Review Social Sentiment." International Journal of Computer Sciences and Engineering 5.5 (2017): 162-169.

APA Style Citation: A. Sharma, Iyapparaja M., (2017). Prediction of Online Products Rating Using Textual Review Social Sentiment. International Journal of Computer Sciences and Engineering, 5(5), 162-169.

BibTex Style Citation:
@article{Sharma_2017,
author = {A. Sharma, Iyapparaja M.},
title = {Prediction of Online Products Rating Using Textual Review Social Sentiment},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2017},
volume = {5},
Issue = {5},
month = {5},
year = {2017},
issn = {2347-2693},
pages = {162-169},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1284},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1284
TI - Prediction of Online Products Rating Using Textual Review Social Sentiment
T2 - International Journal of Computer Sciences and Engineering
AU - A. Sharma, Iyapparaja M.
PY - 2017
DA - 2017/05/30
PB - IJCSE, Indore, INDIA
SP - 162-169
IS - 5
VL - 5
SN - 2347-2693
ER -

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Abstract

It exhibits a magnificent opportunity to share our perspectives for various trading website give it to buy. Be that as it may, give it confront the knowledge overloading disadvantage. The route to mine significant information from reviews to get a handle on a client`s inclinations and make a right proposal is critical. Old recommender technique examine a few elements, similar to client`s buy records, item class, and geographic area. Amid this work, here we have a trend to propose a social user sentiment prediction technique in recommender technique. Than here we have a trend to propose a social client nostalgic measuring approach and ascertain each client`s notion on things/items. Also, here we have a trend to not exclusively to think a client`s own sentimental attribute however conjointly take social sentimental influence into thought. At that point, we tend to think item name, which may be gathered by the sentimental distribution of a client set that reproduce clients` complete examination. Finally, we tend to circuit 3 variables client supposition similitude, social sentimental influence, and thing`s name likeness into our recommender technique to shape a right evaluating prediction.

Key-Words / Index Term

Review, Prediction, Item, Sentiment, recommendation, Rating, System

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