Asia Pacific University Library catalogue


Sentiment analysis in social networks / edited by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu.

Contributor(s): Pozzi, Federico Alberto [editor.] | Fersini, Elisabetta [editor.] | Messina, Enza [editor.] | Liu, Bing [editor.]Material type: TextTextPublication details: Cambridge, MA : Morgan Kaufmann, [2017]; ℗♭2017Description: xix, 263 p. : ill. ; 24 cmISBN: 9780128044124 (pbk.); 0128044128 (pbk.)Subject(s): Natural language processing (Computer science) | Computational linguistics | Social networksDDC classification: 006.3/5 LOC classification: QA76.9.N38 | S46 2017
Contents:
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.
Summary: 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.
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Item type Current library Collection Call number Copy number Status Date due Barcode
General Circulation General Circulation APU Library
Open Shelf 		Book QA76.9.N38 S46 2017 c.1 (Browse shelf (Opens below)) 1 Available 00012318

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.

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