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Hands-On Artificial Intelligence for Banking

You're reading from  Hands-On Artificial Intelligence for Banking

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781788830782
Pages 240 pages
Edition 1st Edition
Languages
Authors (2):
Jeffrey Ng Jeffrey Ng
Profile icon Jeffrey Ng
Subhash Shah Subhash Shah
Profile icon Subhash Shah
View More author details
Toc

Table of Contents (14) Chapters close

Preface 1. Section 1: Quick Review of AI in the Finance Industry
2. The Importance of AI in Banking 3. Section 2: Machine Learning Algorithms and Hands-on Examples
4. Time Series Analysis 5. Using Features and Reinforcement Learning to Automate Bank Financing 6. Mechanizing Capital Market Decisions 7. Predicting the Future of Investment Bankers 8. Automated Portfolio Management Using Treynor-Black Model and ResNet 9. Sensing Market Sentiment for Algorithmic Marketing at Sell Side 10. Building Personal Wealth Advisers with Bank APIs 11. Mass Customization of Client Lifetime Wealth 12. Real-World Considerations 13. Other Books You May Enjoy

Understanding sentiment analysis

Sentiment analysis is a technique in which text mining is done for contextual information. The contextual information is identified and extracted from the source material. It helps businesses understand the sentiment for their products, securities, or assets. It can be very effective to use the advanced techniques of artificial intelligence for in-depth research in the area of text analysis. It is important to classify the transactions around the following concepts:

  • The aspect of security the buyers and sellers care about
  • Customers' intentions and reactions concerning the securities

Sentiment analysis is known to be the most common text analysis and classification tool. It receives an incoming message or transaction and classifies it depending on whether the sentiment associated with the transaction is positive, negative, or neutral. By using the sentiment analysis technique, it is possible to...

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