About Dataset

Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.

• Number of reviews: 568,454
• Number of users: 256,059
• Number of products: 74,258
• Timespan: Oct 1999 - Oct 2012
• Number of Attributes/Columns in data: 10

Attribute Information:

• Id
• ProductId - unique identifier for the product
• UserId - unqiue identifier for the user
• ProfileName
• HelpfulnessNumerator - number of users who found the review helpful
• HelpfulnessDenominator - number of users who indicated whether they found the * review helpful or not
• Score - rating between 1 and 5
• Time - timestamp for the review
• Summary - brief summary of the review
• Text - text of the review

Objective:

Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).

How to determine if a review is positive or negative?

We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.

Analysis of review over time:

The distribution of each classes in the review is shown in the form of graph:

We converted score below 3 as negative and score above 3 as positive review. The distribution can be seen below, which is an imbalanced one

Lets look on the some of the TSNE analysis:

1. TSNE for Bag of Words

2. TSNE for TFIDF

3. TSNE for Average Word2Vec

4. TSNE for TFIDF W2V

Modelling

• The dataset has been run with-

  • Naive Bayes
  
  • Logistic Regression
  
  • Support Vector machine
  
  • Decision tree
  
  • Random Forest
  
  • Xgboost
  

• Click here to run it live on server

How to run?

Clone the repository

https://github.com/Sagor-Saha/Amazon-Fine-Food-Reviews.git

Install the libraries required

pip install -r requirements.txt

run this file to generate the models

Final_solution.ipynb

Now run,

run app.py

🔗 Links