[Retracted] Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Table 4

Comparing the results of an in-domain datasets with existing work.

Paper id	Domain dataset	Features used	Method	Accuracy
Faranak Abri et al. [27]	Restaurant	Linguistic features from review content	MLP	73%
Ren Y et al. [22]	Hotel	Review content and pretrained word embedding (bag of word)	CNN	84%
Barushka et al. [33]	Hotel	Review content with pretrained word embedding (skip-gram)	DFNN	83%
Garcia L. [24]	Amazon	Review content with TF-IDF	SVM	63%
Hajek et al. [19]	Amazon	Review content with pretrained word embedding (skip-gram)	DFFNN; CNN	82%; 81%
Barbado et al. [17]	Yelp	Review content with TF-IDF	AdaBoost	82%
This study	Restaurant; Hotel; Yelp; Amazon	n-grams of the review content with word-embedding matrix using embedding layer	CNN-LSTM	77%; 85%; 86%; 87%