AMAZON-FINE-FOOD-REVIEWS

ABOUT DATA

Context

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.

Contents

Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite database.sqlite: Contains the table 'Reviews'

Data includes:

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

ATTRIBUTES / FEATURES

• 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

OBJECTIVES

SENTIMENTAL ANALYSIS

To predict whether the given review is positive or negative .i.e., 1 or 0

FEATURIZATION

In this we are applying the techniques like

• BAG OF WORDS(BOW)
• Term Frequency–Inverse Document Frequency(TF-IDF)
• WORD2VEC (W2V)
• TF-IDF WORD2VEC(TFIDF W2V)

ALGORITHMS APPLIED TO PREDICT.

- KNN

- Naive Bayes

- Logistic Regression

- Support Vector Machine

- Decision Tree

- XGBoost and RandomForest

- K-means, Agglomerative, DBSCAN Clustering

- Truncated SVD