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

Overview

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

PWC

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:

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.

Owner
Ashish Salunkhe
Full Stack Developer. Interests in NLP, Knowledge Graphs and Product Dev
Ashish Salunkhe
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