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020 _a9783319236544
_9978-3-319-23654-4
024 7 _a10.1007/978-3-319-23654-4
_2doi
035 _ato000560768
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aRC321-580
072 7 _aPSAN
_2bicssc
072 7 _aMED057000
_2bisacsh
082 0 4 _a612.8
_223
100 1 _aCambria, Erik.
_eauthor.
_9451154
245 1 0 _aSentic Computing
_helectronic resource
_bA Common-Sense-Based Framework for Concept-Level Sentiment Analysis /
_cby Erik Cambria, Amir Hussain.
250 _a1st ed. 2015.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXXII, 176 p. 54 illus., 40 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSocio-Affective Computing ;
_v1
505 0 _aIntroduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.
520 _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.
650 0 _amedicine.
_9566220
650 0 _aNeurosciences.
_9302217
650 0 _aData mining.
_9306371
650 0 _aSemantics.
_9309803
650 0 _aCognitive psychology.
_9566360
650 1 4 _aBiomedicine.
_9566246
650 2 4 _aNeurosciences.
_9302217
650 2 4 _aData Mining and Knowledge Discovery.
_9306372
650 2 4 _aSemantics.
_9309803
650 2 4 _aCognitive Psychology.
_9566362
700 1 _aHussain, Amir.
_eauthor.
_9330983
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aSocio-Affective Computing ;
_9467876
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-23654-4
912 _aZDB-2-SBL
999 _c415452