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Sentiment Analysis in Social Networks
SPAR
kr 139
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics
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The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics
Produktdetaljer
Sprog: Engelsk
Sider: 284
ISBN-13: 9780128044124
Indbinding: Paperback
Udgave:
ISBN-10: 0128044128
Udg. Dato: 15 sep 2016
Længde: 16mm
Bredde: 193mm
Højde: 236mm
Oplagsdato: 15 sep 2016
Forfatter(e) Bing (University of Illinois at Chicago Liu, Elisabetta (University of Milano-Bicocca Fersini, Enza (University of Milano-Bicocca Messina, Federico Alberto (SAS Institute Pozzi, Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu


Kategori Netværksprogrammering


Sprog Engelsk


Indbinding Paperback


Sider 284


Udgave


Længde 16mm


Bredde 193mm


Højde 236mm


Udg. Dato 15 sep 2016


Oplagsdato 15 sep 2016

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