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Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings

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Title Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings
 
Creator Deshai, N
Rao, B Bhaskara
 
Subject CNN-LSTM
Glove
LSTM-RNN
One hot encoding
 
Description 120-127
Nowadays, online reviews and ratings are the most valuable source of word-of-mouth, voice-of-customer, and feedback,
also customers can make purchasing decisions on what to buy, where to buy, and what to select. Genuine online reviews are
becoming popular, but unfortunately, we have an issue that might only sometimes be unbiased or accurate. Because most of
the reviews are fake reviews and ratings, these could mislead innocent customers and highly influence customers'
purchasing decisions in the wrong manner. This paper's primary goal is to accurately detect fake reviews and what is the
main difference between them. The secondary goal is to detect fake ratings and actual ratings-based reviews across the
online platform, especially Amazon datasets. The Paper proposes two novel deep-learning Hybrid techniques: CNN-LSTM
for detecting fake online reviews, and LSTM-RNN for detecting fake ratings in the e-commerce domain. Both Hybrid
models can outperform and achieve better performance with the most advanced word embedding techniques, Glove, and
One hot encoding techniques. As per the experimental results, the first technique efficiently detects fake online reviews with
the highest prediction accuracy. The second hybrid model is better than the existing models that detect fake online ratings
with the most excellent precision of 93.8%. The experimental research efficiently revealed that the CNN-LSTM and LSTMRNN
methods are more efficient and practicable and might be better suited for optimal results and maximizing the
efficiency of fake online review detection.
 
Date 2023-01-16T10:05:22Z
2023-01-16T10:05:22Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61198
https://doi.org/10.56042/jsir.v82i1.69937
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]