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"The Pintupi, a hunting-and-gathering people of Australia's Western Desert, were among the last Aborigines to come into contact with white Australians. Anthropologist Fred Myers, who has been working with the Pintupi since 1973, presents an innovative study of this small-scale, spatially dispersed, egalitarian society. His comprehensive ethnography focuses on contradictions between indigenous ideas of individual autonomy and those of "relatedness", a tension mediated in politics, spatial relations, and the mythological construction of The Dreaming. Myers' sophisticated analysis shows how these contraditions shape Pintupi personhood; despite the duress of recent relocation in settlements, these Aboriginal people struggle to define themselves in terms of this cultural logic."
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.
A benchmark in the history of economics and of political ideas, Rothschild shows us the origins of laissez-faire economic thought and its relation to political conseratism in an unquiet world.
This open access book is the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 28th Annual International eTourism Conference, which assembles the latest research presented at the ENTER21@yourplace virtual conference January 19–22, 2021. This book advances the current knowledge base of information and communication technologies and tourism in the areas of social media and sharing economy, technology including AI-driven technologies, research related to destination management and innovations, COVID-19 repercussions, and others. Readers will find a wealth of state-of-the-art insights, ideas, and case studies on how information and communication technologies can be applied in travel and tourism as we encounter new opportunities and challenges in an unpredictable world.
Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.