Sentiment-Analysis-Amazon-Fine-Food-Reviews

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).

Dataset Used

Out of the total dataset, for this study a balanced subset of the dataset was chosen randomy consisting of 5000 positively labelled reviews and 5000 negatively labelled reviews.

Text cleaning tasks done:

• Removing words with HTML tags
• Removing URLs
• Converting all words to lowercase
• Removing all special characters and punctuation marks
• Removing alpha-numeric words
• Removing Stopwords
• Stemming

Techniques used for preprocessing of data:

• Bag of Words
• TF-IDF vectorizer
• Word2Vec: Avg w2v and TF-IDF weighted w2v

Performance metrics

• Accuracy
• Confusion Matrix
• Classification report
• AUC
• ROC curve