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008			181024s2018 gw | s |||| 0|eng d
020			_a9783319950204 _9
024	7		_a10.1007/978-3-319-95020-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aQ342 _b2018 EB
100	1		_aPoria, Soujanya _eautor _9669169
245	1	0	_aMultimodal Sentiment Analysis _cby Soujanya Poria, Amir Hussain, Erik Cambria
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XI, 214 páginas) _b34 ilustraciones, 25 ilustraciones a color
336			_aTexto _btxt _2rdacontent
347			_atext file _bPDF _2rda
490	0		_aSocio-Affective Computing _x2509-5706 _v8
505	0		_aPreface -- Introduction and Motivation -- Background -- Literature Survey and Datasets -- Concept Extraction from Natural Text for Concept Level Text Analysis -- EmoSenticSpace: Dense concept-based affective features with common-sense knowledge -- Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns -- Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis -- Conclusion and Future Work -- Index.
520	3		_aThis latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aHussain, Amir _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/75831747 _986026
700	1		_aCambria, Erik _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2012155591 _1http://viaf.org/viaf/283846500 _993992
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
776	0	8	_iPrinted edition: _z9783319950181
776	0	8	_iPrinted edition: _z9783319950198
776	0	8	_iPrinted edition: _z9783030069568
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-95020-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aBiomedical and Life Sciences (Springer-11642)
942			_2lcc
988			_aEBSPRINGER_BIOMEDLIFE_2019
998			_aSI _a_alco _a_vill _b01/2019 _cm _dz _ef _feng _ggw _h0
999			_c106848 _d106848 _x1