Text mining sentiment analysis (12 résultats)

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PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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HRD. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rising need to address shifting global temperatures, precipitation patterns, and atmospheric conditions, text mining and sentiment analysis play a crucial role in managing climate change and promoting environmental sustainability. These techniques provide valuable insights to support decision-making, stakeholder engagement, risk management, policymaking, and corporate communication efforts to address the changing climate and respond to important crises. Further research into text mining and sentiment analysis is necessary to understand the public's perception on climate change, address corporate concerns, and identify emerging risks associated with the environment. Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability provides updated information on the emergence and role of text mining and sentiment analysis in predicting climate change and promoting environmental sustainability. It covers emerging trends involved in the nexus of text mining, sentiment analysis, climate change and environmental sustainability. This book covers topics such as environmental science, sustainable development, and machine learning, and is a useful resource for climatologists, environmental scientists, computer engineers, data scientists, academicians, and researchers.…

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rising need to address shifting global temperatures, precipitation patterns, and atmospheric conditions, text mining and sentiment analysis play a crucial role in managing climate change and promoting environmental sustainability. These techniques provide valuable insights to support decision-making, stakeholder engagement, risk management, policymaking, and corporate communication efforts to address the changing climate and respond to important crises. Further research into text mining and sentiment analysis is necessary to understand the public's perception on climate change, address corporate concerns, and identify emerging risks associated with the environment. Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability provides updated information on the emergence and role of text mining and sentiment analysis in predicting climate change and promoting environmental sustainability. It covers emerging trends involved in the nexus of text mining, sentiment analysis, climate change and environmental sustainability. This book covers topics such as environmental science, sustainable development, and machine learning, and is a useful resource for climatologists, environmental scientists, computer engineers, data scientists, academicians, and researchers.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).…

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