/Logistic_Regression

Step to step implementation of using logistic regression for sentiment analysis of Amazon fine food review

Primary LanguageJupyter Notebook

Sentiment Analysis using Logistic Regression

Amazon Fine Food Review dataset

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

EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/

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).

    [Q] How to determine if a review is positive or negative?


    [Ans] 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.