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Sentiment Analysis and Ontology Engineering

Overview of attention for book
Cover of 'Sentiment Analysis and Ontology Engineering'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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?
  5. Altmetric Badge
    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
  18. Altmetric Badge
    Chapter 17 Knowledge-Based Tweet Classification for Disease Sentiment Monitoring
Attention for 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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Mentioned by

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1 X user

Citations

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26 Dimensions

Readers on

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9 Mendeley
Summary X Dimensions citations
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Chapter title
Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment
Chapter number 7
Book title
Sentiment Analysis and Ontology Engineering
Published in
Studies in Computational Intelligence, March 2016
DOI 10.1007/978-3-319-30319-2_7
Book ISBNs
978-3-31-930317-8, 978-3-31-930319-2
Authors

Massimiliano Dal Mas

Editors

Witold Pedrycz, Shyi-Ming Chen

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Researcher 2 22%
Student > Bachelor 1 11%
Lecturer > Senior Lecturer 1 11%
Unknown 2 22%
Readers by discipline Count As %
Psychology 3 33%
Linguistics 1 11%
Business, Management and Accounting 1 11%
Computer Science 1 11%
Engineering 1 11%
Other 0 0%
Unknown 2 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 July 2018.
All research outputs
#17,732,227
of 25,992,468 outputs
Outputs from Studies in Computational Intelligence
#1
of 1 outputs
Outputs of similar age
#194,805
of 316,000 outputs
Outputs of similar age from Studies in Computational Intelligence
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.0. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,000 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them

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