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100   $a20151210d2015      u|             0
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200 10$aSentic Computing$b[electronic resource] $eA Common-Sense-Based Framework for Concept-Level Sentiment Analysis /$fby Erik Cambria, Amir Hussain
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (196 p.)
225 1 $aSocio-Affective Computing,$x2509-5706 ;$v1
300   $aDescription based upon print version of record.
311   $a3-319-23653-9 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.
330   $aThis volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: ?    Sentic Computing's multi-disciplinary approach to sentiment  analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference ?    Sentic Computing?s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text ?    Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain  and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.
410  0$aSocio-Affective Computing,$x2509-5706 ;$v1
606   $aNeurosciences
606   $aData mining
606   $aSemantics
606   $aCognitive psychology
606   $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aSemantics$3https://scigraph.springernature.com/ontologies/product-market-codes/N39000
606   $aCognitive Psychology$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20060
615  0$aNeurosciences.
615  0$aData mining.
615  0$aSemantics.
615  0$aCognitive psychology.
615 14$aNeurosciences.
615 24$aData Mining and Knowledge Discovery.
615 24$aSemantics.
615 24$aCognitive Psychology.
676   $a610
700   $aCambria$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut$0885028
702   $aHussain$b Amir$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910298447503321
996   $aSentic Computing$92519135
997   $aUNINA