DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Ashish Salunkhe

Last update: Dec 17, 2022

Overview

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures

Dataset: https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz
https://www.kaggle.com/rtatman/deceptive-opinion-spam-corpus
The data includes 1,569,264 samples from the Yelp Dataset Challenge 2015. This subset has 280,000 training samples and 19,000 test samples in each polarity.
**Also, if you happen to refer my work, a citation would do wonders for me. Thanks! **
The following implementations are done:

Bidirectional LSTM with GLoVE 50D word embeddings
LSTM with GLoVE 100D word embeddings
LSTM with GLoVE 300D word embeddings
CNN-LSTM with Doc2Vec and TF-IDF
Attention mechanism with GLoVe 100D word embeddings
Logistic Regression
Multinomial Naive Bayes
Support Vector Machine - Stochastic Gradient Descent (SGD)

The results obtained were as follows:

Sr. No.	Model Accuracy (%)	Precision Score	Recall Score	F1 Score
1	MultinomialNB	90.25	0.9325	0.8601
2	Stochastic Gradient Descent (SGD)	87.75	0.8913	0.8497
3	Logistic Regression	87.00	0.8691	0.8601
4	Support Vector Machine	56.25	0.525	0.9792
5	Gaussian Naive Bayes	63.5	0.6424	0.6169
6	K-Nearest Neighbour	57.5	0.8604	0.1840
7	Decision tree	68.5	0.6681	0.7412

Model	Training accuracy(%)	Testing accuracy(%)
Bidirectional LSTM + GLoVe(50D)	92.17	88.13
LSTM + GLoVe(100D)	99.18	85.75
CNN + LSTM + Doc2Vec +TF-IDF	96.23	92.19
CNN + Attention + GLoVe(100D)	99.00	90.25
BiLSTM + Attention + GLoVe(100D)	99.18	89.27
CNN + BiLSTM + Attention + GLoVe(100D)	99.75	81.25
LogisticRegression + TF-IDF	99.11	87.21

Future scope includes improvement in the attention layer to increase testing accuracy. BERT and XLNet can be implemented to improve the performance further.

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DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

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Overview

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures

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