Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings
N. Shrestha, und F. Nasoz. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 8 (1):
15(Februar 2019)
DOI: 10.5121/ijscai.2019.8101
Zusammenfassung
Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their
corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed
in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers
can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between
the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were converted to vectors using
paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our
model incorporated both semantic relationship of review text and product information. We also developed a
web service application that predicts the rating score for a submitted review using the trained model and if
there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the
reviewer.
%0 Journal Article
%1 noauthororeditor
%A Shrestha, Nishit
%A Nasoz, Fatma
%D 2019
%J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)
%K Analysis Deep Learning Ratings Reviews Sentiment User and
%N 1
%P 15
%R 10.5121/ijscai.2019.8101
%T Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings
%U http://aircconline.com/ijscai/V8N1/8119ijscai01.pdf
%V 8
%X Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their
corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed
in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers
can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between
the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were converted to vectors using
paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our
model incorporated both semantic relationship of review text and product information. We also developed a
web service application that predicts the rating score for a submitted review using the trained model and if
there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the
reviewer.
@article{noauthororeditor,
abstract = {Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their
corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed
in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers
can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between
the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were converted to vectors using
paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our
model incorporated both semantic relationship of review text and product information. We also developed a
web service application that predicts the rating score for a submitted review using the trained model and if
there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the
reviewer.},
added-at = {2019-03-12T13:43:51.000+0100},
author = {Shrestha, Nishit and Nasoz, Fatma},
biburl = {https://www.bibsonomy.org/bibtex/2591a884b1f4ff2ebd6ddfc76f146a781/leninsha},
doi = {10.5121/ijscai.2019.8101},
interhash = {ab18147097fbf640c981ea654005fda0},
intrahash = {591a884b1f4ff2ebd6ddfc76f146a781},
issn = {23191015},
journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)},
keywords = {Analysis Deep Learning Ratings Reviews Sentiment User and},
language = {English},
month = {February},
number = 1,
pages = 15,
timestamp = {2019-03-12T13:43:51.000+0100},
title = {Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings },
url = {http://aircconline.com/ijscai/V8N1/8119ijscai01.pdf},
volume = 8,
year = 2019
}