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:

1. Bidirectional LSTM with GLoVE 50D word embeddings
2. LSTM with GLoVE 100D word embeddings
3. LSTM with GLoVE 300D word embeddings
4. CNN-LSTM with Doc2Vec and TF-IDF
5. Attention mechanism with GLoVe 100D word embeddings
6. Logistic Regression
7. Multinomial Naive Bayes
8. Support Vector Machine - Stochastic Gradient Descent (SGD)

The results obtained were as follows:

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