Abstract
In recent years, the use of Sentiment Analysis (SA) has proliferated across a wide variety of fields, including financial markets and services. SA can help predict market trends, detect changes in consumer behavior, and identify potential investment opportunities. In this chapter, we explore the impact and applications of SA in financial markets and services and review both traditional and state-of-the-art algorithms for sentiment analysis. We demonstrate how SA approaches can help make better decisions and predictions in financial markets, stock exchange, trading, and cryptocurrencies. We provide examples of popular algorithms used in financial SA and discuss their pros and cons. In addition, we mention the main metrics for evaluating SA performance. By the end of this chapter, readers will have a deeper understanding of how sentiment analysis can contribute to financial markets and services, and the tools and techniques used to achieve accurate and reliable results.
| Original language | English |
|---|---|
| Title of host publication | Transformations in Banking, Finance and Regulation |
| Publisher | World Scientific |
| Pages | 207-234 |
| Number of pages | 28 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
Publication series
| Name | Transformations in Banking, Finance and Regulation |
|---|---|
| Volume | 15 |
| ISSN (Print) | 2752-5821 |
| ISSN (Electronic) | 2752-583X |
Bibliographical note
Publisher Copyright:© 2024 World Scientific Publishing Europe Ltd.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 12 Responsible Consumption and Production
Keywords
- Sentiment analysis
- artificial intelligence (AI)
- financial markets
- financial services
- machine learning
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