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Automatic Review Analyzer

Implemented and compared three types of linear classifiers to use for sentiment analysis of Amazon product reviews.

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. The training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

Below are two example entries from the dataset. Each entry consists of the review and its label. The two reviews were written by different customers describing their experience with a sugar-free candy.

Review label
Nasty No flavor. The candy is just red, No flavor. Just plan and chewy. I would never buy them again −1
YUMMY! You would never guess that they're sugar-free and it's so great that you can eat them pretty much guilt free! i was so impressed that i've ordered some for myself (w dark chocolate) to take to the office. These are just EXCELLENT! 1

In order to automatically analyze reviews, I did the following tasks:

  • Considered three classifiers: the perceptron algorithm, the average perceptron algorithm, and the Pegasos algorithm.
  • Use these classifiers on the food review dataset, using some simple text features.
  • Experiment with additional features and explore their impact on classifier performance.

Setup Details:

For this project, I used Python 3.6 with some additional libraries. I strongly recommend that you take note of how the NumPy numerical library is used in the code provided, and read through the on-line NumPy tutorial. NumPy arrays are much more efficient than Python's native arrays when doing numerical computation. In addition, using NumPy will substantially reduce the lines of code you will need to write.

  1. Note on software: For this project, you will need the NumPy numerical toolbox, and the matplotlib plotting toolbox.

  2. Clone/download the repo and unzip into a working directory. The Automatic Sentiment Analysis from Reviews using Linear Classifiers folder contains the following:

  • dataset folder which contains various data files in .tsv format
  • project1.py contains various useful functions and function templates that I used to implement the learning algorithms.
  • main.py is a script skeleton where I called these functions and ran my experiments.
  • utils.py contains various utility functions used throughout the project
  • test.py is a script which runs tests on a few of the methods I implemented. Note that these tests are provided just to help debug the implementation. Feel free to add more test cases locally to further validate the correctness of the code.
  • main.ipynb is the same as main.py and is just for viewing the results of the classifiers.

How to Test Locally:

In your terminal, navigate to the directory where your project files reside. Execute the command python test.py to run all the available tests.

How to Run the Project 1 Functions Locally:

In your terminal, enter python main.py.