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100   $a20181024d2018      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMultimodal Sentiment Analysis$b[electronic resource] /$fby Soujanya Poria, Amir Hussain, Erik Cambria
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (223 pages)
225 1 $aSocio-Affective Computing,$x2509-5706 ;$v8
311   $a3-319-95018-5 
327   $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.
330   $aThis latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.
410  0$aSocio-Affective Computing,$x2509-5706 ;$v8
606   $aNeurosciences
606   $aMultimedia information systems
606   $aOptical data processing
606   $aNatural language processing (Computer science)
606   $aTranslation and interpretation
606   $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006
606   $aMultimedia Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I18059
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040
606   $aTranslation$3https://scigraph.springernature.com/ontologies/product-market-codes/N47000
615  0$aNeurosciences.
615  0$aMultimedia information systems.
615  0$aOptical data processing.
615  0$aNatural language processing (Computer science).
615  0$aTranslation and interpretation.
615 14$aNeurosciences.
615 24$aMultimedia Information Systems.
615 24$aImage Processing and Computer Vision.
615 24$aNatural Language Processing (NLP).
615 24$aTranslation.
676   $a006.3
700   $aPoria$b Soujanya$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064283
702   $aHussain$b Amir$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aCambria$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910298407903321
996   $aMultimodal Sentiment Analysis$92537283
997   $aUNINA