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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Mr. Ranjan Satapathy is currently pursuing Ph.D., at the School of Computer Science and Engg., NTU Singapore under the supervision of Dr. Erik Cambria. His major research interests are deep learning, sentiment analysis and natural language processing. He completed his Bachelor's degree in Computer Science and Engg., from IIIT-Bhubaneswar, India in 2013. He further recieved a M.Tech degree from University of Hyderabad, India in 2016, with majors in Computer Science. During his pursuits of Master's degree, he joined Dr. Cambria's research group SenticNet as an intern, where he worked on bio-medical sentiment analysis. This exposure and a keen-to-learn attitude motivated him to apply for Ph.D under Dr. Cambria.
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„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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Taschenbuch. Zustand: Neu. Sentic Computing | A Common-Sense-Based Framework for Concept-Level Sentiment Analysis | Erik Cambria (u. a.) | Taschenbuch | Socio-Affective Computing | xxii | Englisch | 2019 | Springer | EAN 9783319795164 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 115103174
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