Sentiment Analysis in Social Networks

✍ Scribed by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu

• Publisher: Morgan Kaufmann
• Year: 2017
• Tongue: English
• Leaves: 262
• Edition: 1
• Category: Library

✦ Synopsis

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 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

• Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies
• Provides insights into opinion spamming, reasoning, and social network mining
• Shows how to apply opinion mining 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

✦ Table of Contents

Content:
Front Matter,Copyright,Contributors,Editors’ Biographies,Preface,AcknowledgmentsEntitled to full textChapter 1 - Challenges of Sentiment Analysis in Social Networks: An Overview, Pages 1-11
Chapter 2 - Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis, Pages 13-29
Chapter 3 - Semantic Aspects in Sentiment Analysis, Pages 31-48
Chapter 4 - Linked Data Models for Sentiment and Emotion Analysis in Social Networks, Pages 49-69
Chapter 5 - Sentic Computing for Social Network Analysis, Pages 71-90
Chapter 6 - Sentiment Analysis in Social Networks: A Machine Learning Perspective, Pages 91-111
Chapter 7 - Irony, Sarcasm, and Sentiment Analysis, Pages 113-128
Chapter 8 - Suggestion Mining From Opinionated Text, Pages 129-139
Chapter 9 - Opinion Spam Detection in Social Networks, Pages 141-156
Chapter 10 - Opinion Leader Detection, Pages 157-170
Chapter 11 - Opinion Summarization and Visualization, Pages 171-187
Chapter 12 - Sentiment Analysis With SpagoBI, Pages 189-195
Chapter 13 - SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies, Pages 197-209
Chapter 14 - The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns*, Pages 211-222
Chapter 15 - Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements With Real-Time Multidimensional Opinion Streaming, Pages 223-237
Chapter 16 - Conclusion and Future Directions, Pages 239-241
Author Index, Pages 243-255
Subject Index, Pages 257-263

✦ Subjects

Natural language processing (Computer science)