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  3. Recurrent Neural Networks (RNN) for Language Modeling in Python

Exercise

Sentiment analysis

In the video exercise, you were exposed to the various applications of sequence to sequence models. In this exercise you will see how to use a pre-trained model for sentiment analysis.

The model is pre-loaded in the environment on variable model. Also, the tokenized test set variables X_test and y_test and the pre-processed original text data sentences from IMDb are also available.You will learn how to pre-process the text data and how to create and train the model using Keras later in the course.

You will use the pre-trained model to obtain predictions of sentiment. The model returns a number between zero and one representing the probability of the sentence to have a positive sentiment. So, you will create a decision rule to set the prediction to positive or negative.

Instructions

100 XP
  • Use the .predict() method to make predictions on the test data.
  • Make the prediction equal to "positive" if its value is greater than 0.5 and "negative" otherwise and store the result in the pred_sentiment variable.
  • Create a pd.DataFrame containing the pre-processed text, the prediction obtained in the previous step and their true values contained in the y_test variable.
  • Print the first rows using the .head() method.