Sentiment analysis is another widely studied research area in natural language processing (NLP). It's a popular task performed on reviews to determine the sentiments of comments provided by reviewers. In this example, we'll focus on analyzing movie review data from the Internet Movie Database (IMDb) and classifying it according to whether it is positive or negative.

We have movie reviews in .txt files that are separated into two folders: negative and positive. There are 1,000 positive reviews and 1,000 negative reviews. These files can be retrieved from GitHub.

We have divided this case study into two parts:

• The first part is to prepare the dataset. We'll read the review files that are provided in the .txt format, append them, label them as positive or negative based on which folder they have been put in, and create a .csv file that contains the label and text.
• In the second part, we'll build multiple base learners on both the count data and on the TF-IDF data. We'll evaluate the performance of the base learners and then evaluate the ensemble of the predictions.