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Ensemble Machine Learning Cookbook

You're reading from   Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Length 336 pages
Edition 1st Edition
Languages
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Authors (2):
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Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Author Profile Icon Vijayalakshmi Natarajan
Vijayalakshmi Natarajan
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Table of Contents (14) Chapters Close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning FREE CHAPTER 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Sentiment analysis of movie reviews using an ensemble model

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...
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