Sentiment Analysis of Amazon Fine Food Reviews using classical machine learning models

Amazon Fine Food Reviews Analysis 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

Input :

Reviews text and Reviews Score.

Output :

ROC AUC of various machine learning models.

Requirements

• sklearn libraries
• each ipynb contains all the necessary imports kindly check each ipynb

Run Locally

Clone the project

  git clone https://link-to-project

Go to the project directory

  cd my-project

Install dependencies

  install above packages

Run main file

  Run each file in jupyter notebook

Conclusion

                  KNN (K Nearest Neighbor)

                  LR (Logistic regression)

                  MNB (Multinomial Naive Bayes)

                  SVM (Support Vector Machine)

                  DT (Decision Tree)

         RFGBDT(Random Forest and Gradient Boosting)

Authors

• @shubendu

Acknowledgements

• Applied AI