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COSC-6339-HW3 - Team 12

Team Members

Kapoor, Kartik 2462
Nham, Bryan 2494
Panyala, Sukrutha 8740
Vohra, Vedant 2889

Dataset

amazon_reviews_grocery.tsv / amazon_reviews_grocery_100k.tsv

Approach

Preprocessing (using PySpark)

Column filtering, mapping categorical values to numerical, text cleaning, stop-word removal, lemmatization, tokenization, converting to vectors and creating word embeddings

Models

Bi-directional LSTM w/ Attention

  1. A model that only uses the review text as input
  2. A model that uses the review text as well as some of the other numerical/categorical features as input (helpful_votes, total_votes, vine, verified_purchase)
    -> The additional features are added as a second Input layer which is concatenated with the output from the LSTM, just before the Dense layers.

Evaluation

Accuracy metric + confusion matrix. Compared with fine-tuned BERT model (Refer Model comparison section for details)

How to run

  1. Install dependencies
pip3 install -r requirements.txt
  1. Install nltk data
python3 -m nltk.downloader stopwords wordnet omw-1.4
  1. Run pyspark pre-processing job to generate datasets + embeddings for training and testing (stored in data/)
spark-submit preprocess.py <absolute_file_path>

(absolute_file_path path here can be a hdfs path like: "hdfs://10.0.1.111:9000/amazon_reviews_grocery_100k.tsv")

  1. Train models (optional, as trained models are already present in models/. Skip to next step)
python3 train.py
  1. Evaluate models
python3 test.py

Model comparison

We have evaluated this model against the state-of-the-art Transformer-based model, BERT, trained via transfer-learning and fine-tuned on our dataset.
This approach doesn't require any pre-processing on the text, since BERT does it's own tokenization and embedding.
The complete code for our Bi-directional LSTM model and the BERT model as well as the evaluation of those models can be found in the notebook hw3.ipynb (or access it here: https://github.com/JediRhymeTrix/COSC-6339-HW3/blob/master/hw3.ipynb)