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Hands-On Ensemble Learning with Python

You're reading from   Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789612851
Length 298 pages
Edition 1st Edition
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Authors (2):
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Konstantinos G. Margaritis Konstantinos G. Margaritis
Author Profile Icon Konstantinos G. Margaritis
Konstantinos G. Margaritis
George Kyriakides George Kyriakides
Author Profile Icon George Kyriakides
George Kyriakides
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Required Software Tools FREE CHAPTER
2. A Machine Learning Refresher 3. Getting Started with Ensemble Learning 4. Section 2: Non-Generative Methods
5. Voting 6. Stacking 7. Section 3: Generative Methods
8. Bagging 9. Boosting 10. Random Forests 11. Section 4: Clustering
12. Clustering 13. Section 5: Real World Applications
14. Classifying Fraudulent Transactions 15. Predicting Bitcoin Prices 16. Evaluating Sentiment on Twitter 17. Recommending Movies with Keras 18. Clustering World Happiness 19. Another Book You May Enjoy

Evaluating Sentiment on Twitter

Twitter is a highly popular social network with over 300 million monthly active users. The platform has been developed around short posts (limited to a number of characters; currently, the limit is 280 characters). The posts themselves are called tweets. On average, 6000 tweets are tweeted every second, which equates to around 200 billion tweets per year. This constitutes a huge amount of data that contains an equal amount of information. As is obvious, it is not possible to analyze this volume of data by hand. Thus, automated solutions have been employed, both by Twitter and third parties. One of the hottest topics involves a tweet's sentiment, or how the user feels about the topic that they tweets. Sentiment analysis comes in many flavors. The most common approach is a positive or negative classification of each tweet. Other approaches involve...

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