Lesson 24: Sentiment Analysis — Your First NLP Model

Ready to build your first NLP model? Let's analyze sentiment!

Sentiment Analysis is the process of predicting whether a piece of text expresses a positive or negative emotion. It's widely used by companies to automatically gauge customer feedback.

The End-to-End Pipeline

We'll use IMDB movie reviews as our dataset. Here is how our pipeline will look:

Load and explore the data.
Preprocess text using a TfidfVectorizer to convert words to numbers.
Split the data into training and testing sets.
Train a classifier. We'll use Logistic Regression, which is like the linear regression of classification.
Evaluate the model using accuracy, precision, recall, and a confusion matrix.

Coding Challenge: Predict Your Own Review

Once your model is trained, it's time to test it with your own words!

Assume you have a trained model named model and a fitted vectorizer named vectorizer.
Write a custom movie review: my_review = ["The plot was dull and the acting was terrible."]
Transform it using the vectorizer: my_review_vectorized = vectorizer.transform(my_review)
Predict the sentiment: prediction = model.predict(my_review_vectorized)
Print the prediction. Did it get it right?