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Coronavirus News, Markets and AI explores the analysis of unstructured data from coronavirus related news and the underlying sentiment during its real-time impact on the world and on global financial markets, in particular.
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Coronavirus News, Markets and AI explores the analysis of unstructured data from coronavirus related news and the underlying sentiment during its real-time impact on the world and on global financial markets, in particular.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 214
- Erscheinungstermin: 28. Dezember 2020
- Englisch
- Abmessung: 233mm x 156mm x 20mm
- Gewicht: 356g
- ISBN-13: 9780367687724
- ISBN-10: 0367687720
- Artikelnr.: 60354068
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 214
- Erscheinungstermin: 28. Dezember 2020
- Englisch
- Abmessung: 233mm x 156mm x 20mm
- Gewicht: 356g
- ISBN-13: 9780367687724
- ISBN-10: 0367687720
- Artikelnr.: 60354068
Pankaj Sharma is a partner at EMAlpha, a data analytics and investment management firm focused on making Emerging Markets (EMs) accessible to global investors and unlocking EM investing using machines. EMAlpha's focus is on Unstructured Data as the EMs are particularly susceptible to swings in news flow driven investor sentiment. Pankaj has 20 years of diverse work experience in various leadership roles with global investment banks, Indian equity brokerages, state-owned enterprises and start-ups. Earlier, he was a ranked equity analyst and tracked multiple sectors at UBS, Citi and JP Morgan. Pankaj has also been a regular contributor to print and electronic media. For Routledge, he has so far co-authored the following two titles; Artificial Intelligence: Evolution, Ethics and Public Policy and Big Data: A Beginner's Introduction. Pankaj published his first two books in 2017 and they were: Demonetization: Modi's Political Masterstroke and 2019: Will Modi Win? This was followed by Rafale, Raga, Reuniting Forces for 2019 and The Anatomy of an Indian General Election. Pankaj is an engineer from IIT Kharagpur, India, with an MBA from the Faculty of Management Studies, University of Delhi, India.
About this book. Introduction. Part I: The Method 1. How to Read this Book?
2. Reading Coronavirus News 3. Sentiment Analysis, Big Data and AI 4.
Unstructured Data. How to tame the beast? Part II: The Results 5. Ebbing in
May. 'Are we celebrating too early?' 6. The Deadly April. 'Blame Game and
Search for a Coronavirus Vaccine' 7. Coronavirus goes Global in March.
'Oops...It is getting serious' 8. Build-up in February. 'Come on, Don't
worry too much' Part III: The Samples 9. Politics, Conspiracy Theories and
Religion 10. Coronavirus Pandemic's Economic Impact 11. Disease,
Devastation and Hope 12. The Human Nature and Impact on Normal Life 13.
Bizarre, Funny and Fake News Part IV: The Inference 14. Country Sentiment
for Coronavirus News 15. COVID-19 has turned the world upside down 16.
What is seen more often in Coronavirus News? 17. How do we use Sentiment
Analysis. Case Study 18. Conclusion
2. Reading Coronavirus News 3. Sentiment Analysis, Big Data and AI 4.
Unstructured Data. How to tame the beast? Part II: The Results 5. Ebbing in
May. 'Are we celebrating too early?' 6. The Deadly April. 'Blame Game and
Search for a Coronavirus Vaccine' 7. Coronavirus goes Global in March.
'Oops...It is getting serious' 8. Build-up in February. 'Come on, Don't
worry too much' Part III: The Samples 9. Politics, Conspiracy Theories and
Religion 10. Coronavirus Pandemic's Economic Impact 11. Disease,
Devastation and Hope 12. The Human Nature and Impact on Normal Life 13.
Bizarre, Funny and Fake News Part IV: The Inference 14. Country Sentiment
for Coronavirus News 15. COVID-19 has turned the world upside down 16.
What is seen more often in Coronavirus News? 17. How do we use Sentiment
Analysis. Case Study 18. Conclusion
About this book. Introduction. Part I: The Method 1. How to Read this Book?
2. Reading Coronavirus News 3. Sentiment Analysis, Big Data and AI 4.
Unstructured Data. How to tame the beast? Part II: The Results 5. Ebbing in
May. 'Are we celebrating too early?' 6. The Deadly April. 'Blame Game and
Search for a Coronavirus Vaccine' 7. Coronavirus goes Global in March.
'Oops...It is getting serious' 8. Build-up in February. 'Come on, Don't
worry too much' Part III: The Samples 9. Politics, Conspiracy Theories and
Religion 10. Coronavirus Pandemic's Economic Impact 11. Disease,
Devastation and Hope 12. The Human Nature and Impact on Normal Life 13.
Bizarre, Funny and Fake News Part IV: The Inference 14. Country Sentiment
for Coronavirus News 15. COVID-19 has turned the world upside down 16.
What is seen more often in Coronavirus News? 17. How do we use Sentiment
Analysis. Case Study 18. Conclusion
2. Reading Coronavirus News 3. Sentiment Analysis, Big Data and AI 4.
Unstructured Data. How to tame the beast? Part II: The Results 5. Ebbing in
May. 'Are we celebrating too early?' 6. The Deadly April. 'Blame Game and
Search for a Coronavirus Vaccine' 7. Coronavirus goes Global in March.
'Oops...It is getting serious' 8. Build-up in February. 'Come on, Don't
worry too much' Part III: The Samples 9. Politics, Conspiracy Theories and
Religion 10. Coronavirus Pandemic's Economic Impact 11. Disease,
Devastation and Hope 12. The Human Nature and Impact on Normal Life 13.
Bizarre, Funny and Fake News Part IV: The Inference 14. Country Sentiment
for Coronavirus News 15. COVID-19 has turned the world upside down 16.
What is seen more often in Coronavirus News? 17. How do we use Sentiment
Analysis. Case Study 18. Conclusion