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211025 ||| eng |
| 020 |
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|a 1003138977
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| 020 |
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|a 1000327442
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|a 9781000327427
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|a 9781003138976
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|a 1000327426
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|a 9781000327441
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| 020 |
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|a 100032740X
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| 050 |
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4 |
|a HG4515.7
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| 100 |
1 |
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|a SHARMA, PANKAJ.
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| 245 |
0 |
0 |
|a CORONAVIRUS NEWS, MARKETS AND AI
|h [electronic resource]
|b the covid-19 diaries
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| 260 |
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|a [S.l.]
|b ROUTLEDGE INDIA
|c 2020
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| 300 |
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|a 1 online resource
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| 653 |
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|a Information theory in finance / fast
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| 653 |
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|a Théorie de l'information dans les finances
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| 653 |
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|a Intelligence artificielle / Aspect économique
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| 653 |
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|a COVID-19 / Aspect économique
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| 653 |
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 653 |
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|a Big data / fast
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| 653 |
|
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|a Information theory in finance / http://id.loc.gov/authorities/subjects/sh96009571
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| 653 |
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|a COVID-19 (Disease) / Economic aspects
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| 653 |
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|a Stock exchanges and current events / http://id.loc.gov/authorities/subjects/sh85128195
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| 653 |
|
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|a Artificial intelligence / Economic aspects / fast
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| 653 |
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|a Stock exchanges and current events / fast
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| 653 |
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|a Artificial intelligence / Economic aspects
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| 653 |
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|a Bourse et actualité
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| 653 |
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|a Données volumineuses
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| 653 |
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|a COVID-19 (Disease) / Economic aspects / fast
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b OREILLY
|a O'Reilly
|
| 015 |
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|a GBC0G7972
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| 776 |
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|z 0367687720
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| 776 |
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|z 9781000327441
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| 776 |
|
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|z 1000327442
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| 776 |
|
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|z 9780367687724
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| 776 |
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|z 9781000327403
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| 776 |
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|z 9781000327427
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| 776 |
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|z 9780367634131
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| 776 |
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|z 9781003138976
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| 776 |
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|z 0367634139
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781000327441/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 332
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| 082 |
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|a 330.01154
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| 520 |
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|a 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. In an age where information - both real and fake - travels in the blink of an eye and significantly alters market sentiment daily, this book is a blow by blow account of economic impact of the COVID-19 pandemic. The volume: Details how AI driven machines capture, analyse and score relevant on-ground news sentiment to analyse the dynamics of market sentiment, how markets react to good or bad news across 'short term' and 'long term'; Investigates what have been the most prevalent news sentiment during the pandemic, and its linkages to crude oil prices, high profile cases, impact of local news, and even the impact of Trump's policies; Discusses the impact on what people think and discuss, how the COVID-19 crisis differs from the Global Financial Crisis of 2008, the unprecedented disruptions in supply chains and our daily lives; Showcases how easy accessibility to big data methods, cloud computing, and computational methods and the universal applicability of these tool to any topic can help analyse extract the related news sentiment in allied fields. Accessible, nuanced and insightful, this book will be invaluable for business professionals, bankers, media professionals, traders, investors, and investment consultants. It will also be of great interest to scholars and researchers of economics, commerce, science and technology studies, computer science, media and culture studies, public policy and digital humanities
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