Skip to content

Latest commit

 

History

History
53 lines (40 loc) · 1.85 KB

README.md

File metadata and controls

53 lines (40 loc) · 1.85 KB
Raw

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:

evo

  • Confusion Matrix

evo

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.