Sentiment Analysis and Ontology Engineering

Chapter 1 Fundamentals of Sentiment Analysis and Its Applications

Chapter 2 Fundamentals of Sentiment Analysis: Concepts and Methodology

Chapter 3 The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?

Chapter 4 Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction

Chapter 5 Description Logic Class Expression Learning Applied to Sentiment Analysis

Chapter 6 Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

Chapter 7 Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment

Chapter 8 Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework

Chapter 9 Interpretability of Computational Models for Sentiment Analysis

Chapter 10 Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVM \(^{\textit{perf}}\)

Chapter 11 Social Media and News Sentiment Analysis for Advanced Investment Strategies

Chapter 12 Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence

Chapter 13 An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

Chapter 14 Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing

Chapter 15 Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction

Chapter 16 OntoLSA—An Integrated Text Mining System for Ontology Learning and Sentiment Analysis

Chapter 17 Knowledge-Based Tweet Classification for Disease Sentiment Monitoring