Sentiment analysis with GBM

Let's now use scikit-learn's GradientBoostingClassifier on the reviews dataset to predict the sentiment of a review given its text.

We will not pass the raw text as input for the model. The following pre-processing has been done for you:

Remove reviews with missing values.
Select data from the top 5 apps.
Select a random subsample of 500 reviews.
Remove "stop words" from the reviews.
Transform the reviews into a matrix, in which each feature represents the frequency of a word in a review.

Do you want a deeper understanding of text mining? Then go check the course Natural Language Processing Fundamentals in Python!

Build a GradientBoostingClassifier with 100 estimators and a learning rate of 0.1.
Calculate the predictions on the test set.
Compute the accuracy to evaluate the model.
Calculate and print the confusion matrix.

Take Hint (-30 XP)