Amazon Fine Food Review Sentiment Analysis

1.Business Problem:

1.1 Description

1. Dataset is taken from Kaggle
2. This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

1.2 Problem Statemtent:

To determine whether a review is positive or negative and build a machine learning model around it.

2.Dataset:

2.1 Data includes:

• Reviews from Oct 1999 - Oct 2012
• 568,454 reviews
• 256,059 users
• 74,258 products
• 260 users with > 50 reviews

2.2 Columns:

1. Id
2. ProductId - Unique identifier for the product
3. UserId - Unqiue identifier for the user
4. ProfileName - Profile name of user
5. HelpfulnessNumerator - Number of users who found the review helpful
6. HelpfulnessDenominator - Number of users who indicated whether they found the review helpful or not
7. Score - Rating between 1 and 5
8. Time - Timestamp for the review
9. Summary - Brief summary of the review
10. Text - Text of the review

3.Text Preprocessing:

• Convert everything to lowercase
• Remove HTML tags
• Remove URL from sentence
• Contraction mapping
• Eliminate punctuations and special characters
• Remove stopwords
• Remove short words

4.Evolution:

• Confusion Matrix

5.Conclusion:

• Logistic regression with TF-IDF and BOW model are giving more accurate result.
• Also Random Forest with BOW model is giving better result as compare to Naive Bayes.