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Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis Softcover Repri Edition
Contributor(s): Cambria, Erik (Author), Hussain, Amir (Author)
ISBN: 3319795163     ISBN-13: 9783319795164
Publisher: Springer
OUR PRICE:   $104.49  
Product Type: Paperback - Other Formats
Published: March 2019
Qty:
Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Medical | Neuroscience
- Computers | Databases - Data Mining
- Language Arts & Disciplines | Linguistics - Semantics
Dewey: 006
Series: Socio-Affective Computing
Physical Information: 176 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
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 and systems.