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Generating fake reviews

This is part of a project that I've done for a Machine Intelligence course at the Zurich University of Applied Sciences. Since its focus was on big data, Apache Spark is involved at some stages.

What does it do

The idea is to generate fake reviews based on Yelp's review dataset and then try to detect them with more traditional ML methods.

  1. Data wrangling with PySpark, tokenisation with CoreNLP from Stanford
  2. Training a Seq2Seq model with a fork of OpenNMT-py, this part is heavily inspired by Stay On-Topic: Generating Context-specific Fake Restaurant Reviews by Juuti et al.
  3. Training various classifiers with Spark ML that try to distinguish between fake and and real reviews.

Some sample outputs

The Seq2Seq model was able to generate rather convincing looking fake reviews:

SENT 1: ['4.0', 'las', 'vegas', 'restaurants', ',', 'vietnamese']
PRED 1: i love this place ! the food is always fresh and delicious . it 's a little pricey , but worth every penny .
PRED SCORE: -33.2517

SENT 2: ['1.0', 'calgary', 'asian', 'fusion', ',', 'chicken', 'wings', ',', 'food', ',', 'cafes', ',', 'chinese', ',', 'desserts', ',', 'juice', 'bars', '&', 'smoothies', ',', 'restaurants']
PRED 2: this is the worst bubble tea place i 've ever been to . it 's so expensive and they do n't have a lot of options .
PRED SCORE: -36.3755

SENT 3: ['3.0', 'scottsdale', 'japanese', ',', 'nightlife', ',', 'restaurants', ',', 'sushi', 'bars']
PRED 3: this is a good place to go if you 're in the mood for japanese food . it 's not bad , but nothing special .
PRED SCORE: -36.7155

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Generating (and detecting) fake reviews with Seq2Seq / PyTorch and Apache Spark.

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