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.