Sentiment Analysis in Social Networks

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 methodologiesProvides insights into opinion spamming, reasoning, and social network analysisShows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics

Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network miningShows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics

Chapter 1: Challenges of Sentiment Analysis in Social Networks: An Overview

Chapter 2: Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis

Chapter 3: Semantic Aspects in Sentiment Analysis

Chapter 4: Linked Data Models for Sentiment and Emotion Analysis in Social Networks

Chapter 5: Sentic Computing for Social Network Analysis

Chapter 6: Sentiment Analysis in Social Networks: A Machine Learning Perspective

Chapter 7: Irony, Sarcasm, and Sentiment Analysis

Chapter 8: Suggestion Mining From Opinionated Text

Chapter 9: Opinion Spam Detection in Social Networks

Chapter 10: Opinion Leader Detection

Chapter 11: Opinion Summarization and Visualization

Chapter 12: Sentiment Analysis With SpagoBI

Chapter 13: SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies

Chapter 14: The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns

Chapter 15: Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements With Real-Time Multidimensional Opinion Streaming

Chapter 16: Conclusion and Future Directions