Deceptive Spam Review Detection with Convolutional Neural Network in Tensorflow
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
README.md Edit README Aug 20, 2017
data_preprocess.ipynb Deceptive spam reviews detection Aug 20, 2017
eval.ipynb Deceptive spam reviews detection Aug 20, 2017
scnn_model.py Deceptive spam reviews detection Aug 20, 2017
training.ipynb Update training.ipynb Nov 4, 2017

README.md

Deceptive Spam Review Detection with Convolutional Neural Network in Tensorflow

This code belongs to the "DECEPTIVE SPAM REVIEW DETECTION WITH CNN USING TENSORFLOW" blog post.

We implement a model similar to the SCNN model of Luyang Li's Document representation and feature combination for deceptive spam review detection. In that paper, the SCNN model apply convolutional neural network (CNN) technique to detect deceptive spam review.

A Deceptive opinion spam is a review with fictitious opinions which is deliberately written to sound authentic. Deceptive spam review detection can then be thought as of the exercise of taking a review and determining whether is a spam or a truth.

Requirements

  • Python 2.7
  • Jupyter Notebook
  • Tensorflow
  • Numpy

Dataset

We use the first publicly available gold standard corpus of deceptive opinion spam. The dataset consists of truthful and deceptive hotel reviews of 20 Chicago hotels. It contains:

  • 400 truthful positive reviews from TripAdvisor
  • 400 deceptive positive reviews from Mechanical Turk
  • 400 truthful negative reviews from Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor and Yelp
  • 400 deceptive negative reviews from Mechanical Turk.

References

Document representation and feature combination for deceptive spam review detection

Implementing a CNN for Text Classification in TensorFlow

Perform sentiment analysis with LSTMs, using TensorFlow