Library of American University of Madaba
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Sentiment analysis in social networks /
edited by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu.
- Cambridge, MA : Morgan Kaufmann, 2017.
- xix, 263 p. : ill. ; 24 cm.
Includes bibliographical references and indexes.
Challenges of sentiment analysis in social networks: an overview -- Beyond sentiment: how social network analytics can enhance opinion mining and sentiment analysis -- Semantic aspects in sentiment analysis -- Linked data models for sentiment and emotion analysis in social networks -- Sentic computing for social network analysis -- Sentiment analysis in social networks: a machine learning perspective -- Irony, sarcasm, and sentiment analysis -- Suggestion mining from opinionated text -- Opinion spam detection in social networks -- Opinion leader detection -- Opinion summarization and visualization -- Sentiment analysis with SpagoBl -- SOMA: the smart social customer relationship management tool: handling semantic variability of emotion analysis with hybrid technologies -- The human advantage: leveraging the power of predictive analytics to strategically optimize social campaigns -- Price-sensitive ripples and chain reactions: tracking the impact of corporate announcements with real-time multidimensional opinion streaming.
This 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.
9780128044124 0128044128
Natural language processing (Computer science)
Computational linguistics.
Social networks.
QA76.9.N38 / S46 2017
006.35 / S478
Includes bibliographical references and indexes.
Challenges of sentiment analysis in social networks: an overview -- Beyond sentiment: how social network analytics can enhance opinion mining and sentiment analysis -- Semantic aspects in sentiment analysis -- Linked data models for sentiment and emotion analysis in social networks -- Sentic computing for social network analysis -- Sentiment analysis in social networks: a machine learning perspective -- Irony, sarcasm, and sentiment analysis -- Suggestion mining from opinionated text -- Opinion spam detection in social networks -- Opinion leader detection -- Opinion summarization and visualization -- Sentiment analysis with SpagoBl -- SOMA: the smart social customer relationship management tool: handling semantic variability of emotion analysis with hybrid technologies -- The human advantage: leveraging the power of predictive analytics to strategically optimize social campaigns -- Price-sensitive ripples and chain reactions: tracking the impact of corporate announcements with real-time multidimensional opinion streaming.
This 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.
9780128044124 0128044128
Natural language processing (Computer science)
Computational linguistics.
Social networks.
QA76.9.N38 / S46 2017
006.35 / S478