Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis / / by Erik Cambria, Amir Hussain

• Record Nr.: UNINA9910298447503321
• Autore: Cambria Erik
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• ISBN: 3-319-23654-7
• Edizione: [1st ed. 2015.]
• Descrizione fisica: 1 online resource (196 p.)
• Collana: Socio-Affective Computing, , 2509-5706 ; ; 1
• Disciplina: 610
• Soggetti: Neurosciences; Data mining; Semantics; Cognitive psychology; Data Mining and Knowledge Discovery; Cognitive Psychology
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.
• Sommario/riassunto: 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.