Pozzi F. et al. (Eds.) Sentiment Analysis in Social Networks

Morgan Kaufmann, 2017. — 263 p. — ISBN: 978-0-12-804412-4The 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
Academic and industry researchers in artificial intelligence, natural language processing, social networking, networking, and big dataChallenges 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 SpagoBI
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
Conclusion and Future Directions