The data set comprises of fine food reviews from amazon food products. 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).
Loading the data
The dataset is available in two forms
.csv file SQLite Database In order to load the data, I have used the SQLITE dataset as it easier to query the data and visualise the data efficiently.
Here as we only want to get the global sentiment of the recommendations (positive or negative), I purposefully ignored all Scores equal to 3. If the score id above 3, then the recommendation wil be set to "positive". Otherwise, it will be set to "negative".