The Amazon Food Reviews dataset consists of 568,454 food reviews. This dataset consists of a single CSV file, Reviews.csv Both Naive Bayes and Logistic Regression - with L1 regularizor models are used and model with higher accuracy is preffered.
Data Set
Click here to get the dataset.
Review.csv - 251MB
Dataset statistics
Number of reviews 568,454
Number of users 256,059
Number of products 74,258
Users with > 50 reviews 260
Median no. of words per review 56
Timespan Oct 1999 - Oct 2012
Data Fields Explanation
Id - Unique row number
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
Score - rating between 1 and 5
Time - timestamp for the review
Summary - brief summary of the review
Text - text of the review
Analysing the data & plot the required graphs to show that these conclusions are true:
a. Positive reviews are very common. b. Positive reviews are shorter. c. Longer reviews are more helpful. d. Despite being more common and shorter, positive reviews are found more helpful. e. Frequent reviewers are more discerning in their ratings, write longer reviews, and write more helpful reviews
STEP-1: Copy the data in Pandas DataFrame and drop unwanted columns.
STEP-2: Text Preprocessing.
a. Converting to lower-case.
b. Removing HTML Tags.
c. Removing Special Characters.
d. Removing Stop Words.
e. Stemming (Snowball Stemming)
STEP-3: Vectorizing out Data Set
STEP-4: Building and evaluating the model
a. Naive Bayes
b. Logistic Regression - with L1 regularizor
The accuracy of Naive Bayes Model is shown below
The accuracy of Logistic Regression - with L1 regularizor is shown below
