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This editedvolume provides the reader with a fully updated, in-depth treatise on the emergingprinciples, conceptual underpinnings, algorithms and practice of ComputationalIntelligence in the realization of concepts and implementation of models ofsentiment analysis and ontology -oriented engineering.
The volume involves studies devotedto key issues of sentiment analysis, sentiment models, and ontologyengineering. The book is structured into three main parts. The first partoffers a comprehensive and prudently structured exposure to the fundamentals ofsentiment analysis and natural language processing. The second part consists ofstudies devoted to the concepts, methodologies, and algorithmic developmentselaborating on fuzzy linguistic aggregation to emotion analysis, carrying outinterpretability of computational sentiment models, emotion classification,sentiment-oriented information retrieval, a methodology of adaptive dynamics inknowledge acquisition. The third part includes a plethora of applicationsshowing how sentiment analysis and ontologies becomes successfully applied toinvestment strategies, customer experience management, disaster relief,monitoring in social media, customer review rating prediction, and ontologylearning.
This book isaimed at a broad audience of researchers and practitioners. Readers involved inintelligent systems, data analysis, Internet engineering, ComputationalIntelligence, and knowledge-based systems will benefit from the exposure to thesubject matter. The book may also serveas a highly useful reference material for graduate students and seniorundergraduate students.
List of contents
Fundamentalsof Sentiment Analysis andIts Applications.- Fundamentals ofSentiment Analysis: Concepts and Methodology.- The Comprehension of Figurative Language: What is the Influence of Irony and Sarcasm on NLP Techniques?.- ProbabilisticApproaches for Sentiment Analysis: Latent Dirichlet Allocation for OntologyBuilding and Sentiment Extraction.- Description LogicClass Expression Learning Applied to Sentiment Analysis.- Capturing Digest Emotionsby Means of Fuzzy Linguistic Aggregation.- Hyperelastic-basedAdaptive Dynamics Methodology in Knowledge Acquisition for ComputationalIntelligence on Ontology Engineering of Evolving Folksonomy DrivenEnvironment.- Sentiment-Oriented InformationRetrieval: Affective Analysis of Documents Based on the SenticNet Framework.- Interpretability of ComputationalModels for Sentiment Analysis.- Chinese Micro-blog EmotionClassification by Exploiting Linguistic Features and SVMperf.- Social Media and News Sentiment Analysis for Advanced InvestmentStrategies.- ContextAware Customer Experience Management: A Development Framework Based onOntologies and Computational Intelligence.- An Overview ofSentiment Analysis in Social Media and Its Applications in Disaster Relief.- BigData Sentiment Analysis for Brand Monitoring in Social Media Streams by CloudComputing.- Neuro-Fuzzy SentimentAnalysis for Customer Review Rating Prediction.- OntoLSA:An Integrated Text Mining System for Ontology Learning and Sentiment Analysis.- Knowledge-based Tweet Classification for Disease SentimentMonitoring.
Summary
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.
