Sentiment Analysis and Ontology Engineering: An Environment of Computational Intelligence(639 Studies in Computational Intelligence)

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter.  The book may also serve as a highly useful reference material for graduate students and senior undergraduate students. 

Table of Contents:
Fundamentals of Sentiment Analysis and Its Applications.- Fundamentals of Sentiment Analysis: Concepts and Methodology.- The Comprehension of Figurative Language: What is the Influence of Irony and Sarcasm on NLP Techniques?.- Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction.- Description Logic Class Expression Learning Applied to Sentiment Analysis.- Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation.- Hyperelastic-based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment.- Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework.- Interpretability of Computational Models for Sentiment Analysis.- Chinese Micro-blog Emotion Classification by Exploiting Linguistic Features and SVMperf.- Social Media and News Sentiment Analysis for Advanced Investment Strategies.- Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence.- An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief.- Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing.- Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction.- OntoLSA: An Integrated Text Mining System for Ontology Learning and Sentiment Analysis.- Knowledge-based Tweet Classification for Disease Sentiment Monitoring.