Amazon.com, Inc., is an American multinational technology company based in Seattle, Washington. Amazon focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. As they are strong in e-commerce platforms their review system can be abused by sellers or customers writing fake reviews in exchange for incentives. It is expensive to check each and every review manually and label its sentiment. So a better way is to rely on machine learning/deep learning models for that. In this case study, we will focus on the fine food review data set on amazon which is available on Kaggle.

Note: This article is not a code explanation for our problem. Rather I will be explaining the approach I used. You can look at my code from here.

About Data set

The data set consists of reviews of fine foods from amazon over a period of more than 10 years, including 568,454 reviews till October 2012. Reviews include rating, product and user information, and a plain text review. It also includes reviews from all other Amazon categories.

We have the following columns:

1. Product Id: Unique identifier for the product
2. User Id: unique identifier for the user
3. Profile Name: Profile name of the user
4. Helpfulness Numerator: Number of users who found the review helpful
5. Helpfulness Denominator: Number of users who indicated whether they found the review helpful or not
6. Score: Rating between 1 and 5
7. Time: Timestamp
8. Summary: Summary of the review
9. Text: Review

#machine-learning #deeplearing #flask #deployment #sentiment-analysis