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Machine Learning for Finance

You're reading from   Machine Learning for Finance Principles and practice for financial insiders

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789136364
Length 456 pages
Edition 1st Edition
Languages
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Authors (2):
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Jannes Klaas Jannes Klaas
Author Profile Icon Jannes Klaas
Jannes Klaas
James Le James Le
Author Profile Icon James Le
James Le
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Table of Contents (15) Chapters Close

Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
1. Neural Networks and Gradient-Based Optimization 2. Applying Machine Learning to Structured Data FREE CHAPTER 3. Utilizing Computer Vision 4. Understanding Time Series 5. Parsing Textual Data with Natural Language Processing 6. Using Generative Models 7. Reinforcement Learning for Financial Markets 8. Privacy, Debugging, and Launching Your Products 9. Fighting Bias 10. Bayesian Inference and Probabilistic Programming Index

A text classification task


A common NLP task is to classify text. The most common text classification is done in sentiment analysis, where texts are classified as positive or negative. In this section, we will consider a slightly harder problem, classifying whether a tweet is about an actual disaster happening or not.

Today, investors have developed a number of ways to gain information from tweets. Twitter users are often faster than news outlets to report disasters, such as a fire or a flood. In the case of finance, this speed advantage can be used and translated to event-driven trading strategies.

However, not all tweets that contain words associated with disasters are actually about disasters. A tweet such as, "California forests on fire near San Francisco" is a tweet that should be taken into consideration, whereas "California this weekend was on fire, good times in San Francisco" can safely be ignored.

The goal of the task here is to build a classifier that separates the tweets that relate...

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