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<p>Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings</p>

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Title Statement <p>Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings</p>
 
Added Entry - Uncontrolled Name Deshai, N ; Department of C.S.E.Gitam IT, Visakhapatnam, 530 045, Andhra Pradesh, India
Rao, B Bhaskara; Department of C.S.E.Gitam IT, Visakhapatnam, 530 045, Andhra Pradesh, India
 
Uncontrolled Index Term CNN-LSTM, Glove, LSTM-RNN, One hot encoding
 
Summary, etc. <p>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 LSTM-RNN methods are more efficient and practicable and might be better suited for optimal results and maximizing the efficiency of fake online review detection<em>.</em></p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2023-01-19 11:12:04
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/69937
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 82, ##issue.no## 1 (2023)
 
Language Note en