Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•  Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference
•  Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text
•  Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.
1122345626
Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•  Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference
•  Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text
•  Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.
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Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

Hardcover(1st ed. 2015)

$109.99 
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Overview

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•  Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference
•  Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text
•  Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.

Product Details

ISBN-13: 9783319236537
Publisher: Springer International Publishing
Publication date: 12/11/2015
Series: Socio-Affective Computing , #1
Edition description: 1st ed. 2015
Pages: 176
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Introduction.- SenticNet.- Sentic Patterns.- Sentic Applications.- Conclusion.- Index.

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