About this book
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.
- Book Title Sentic Computing
- Book Subtitle A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
- Series Title Socio-Affective Computing
- Series Abbreviated Title Socio-Affect. Comput.
- DOI https://doi.org/10.1007/978-3-319-23654-4
- Copyright Information Springer International Publishing Switzerland 2015
- Publisher Name Springer, Cham
- eBook Packages Biomedical and Life Sciences Biomedical and Life Sciences (R0)
- Hardcover ISBN 978-3-319-23653-7
- Softcover ISBN 978-3-319-79516-4
- eBook ISBN 978-3-319-23654-4
- Edition Number 1
- Number of Pages XXII, 176
- Number of Illustrations 14 b/w illustrations, 40 illustrations in colour
Data Mining and Knowledge Discovery
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