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Sentiment Analysis and Ontology Engineering

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Cover of 'Sentiment Analysis and Ontology Engineering'

Table of Contents

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    Book Overview
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    Chapter 1 Fundamentals of Sentiment Analysis and Its Applications
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    Chapter 2 Fundamentals of Sentiment Analysis: Concepts and Methodology
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    Chapter 3 The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?
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    Chapter 4 Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction
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    Chapter 5 Description Logic Class Expression Learning Applied to Sentiment Analysis
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    Chapter 6 Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation
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    Chapter 7 Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment
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    Chapter 8 Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework
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    Chapter 9 Interpretability of Computational Models for Sentiment Analysis
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    Chapter 10 Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVM \(^{\textit{perf}}\)
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    Chapter 11 Social Media and News Sentiment Analysis for Advanced Investment Strategies
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    Chapter 12 Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence
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    Chapter 13 An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief
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    Chapter 14 Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing
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    Chapter 15 Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction
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    Chapter 16 OntoLSA—An Integrated Text Mining System for Ontology Learning and Sentiment Analysis
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    Chapter 17 Knowledge-Based Tweet Classification for Disease Sentiment Monitoring
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Mentioned by

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8 tweeters

Citations

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16 Dimensions

Readers on

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148 Mendeley
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Title
Sentiment Analysis and Ontology Engineering
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-30319-2
ISBNs
978-3-31-930317-8, 978-3-31-930319-2
Editors

Witold Pedrycz, Shyi-Ming Chen

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 <1%
India 1 <1%
Ireland 1 <1%
Singapore 1 <1%
Unknown 144 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 23%
Student > Master 31 21%
Student > Bachelor 21 14%
Student > Doctoral Student 10 7%
Researcher 8 5%
Other 14 9%
Unknown 30 20%
Readers by discipline Count As %
Computer Science 74 50%
Business, Management and Accounting 13 9%
Engineering 8 5%
Linguistics 5 3%
Social Sciences 4 3%
Other 11 7%
Unknown 33 22%