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Multimodal sentiment analysis

Cambria, Erik.

Multimodal sentiment analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal sentiment analysisby Soujanya Poria, Amir Hussain, Erik Cambria.
Author:	Poria, Soujanya.
other author:	Hussain, Amir.
Published:	Cham :Springer International Publishing :2018.
Description:	xi, 214 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computational intelligence.
Online resource:	https://doi.org/10.1007/978-3-319-95020-4
ISBN:	9783319950204$q(electronic bk.)

Multimodal sentiment analysis
Poria, Soujanya.

Multimodal sentiment analysis[electronic resource] /by Soujanya Poria, Amir Hussain, Erik Cambria. - Cham :Springer International Publishing :2018. - xi, 214 p. :ill. (some col.), digital ;24 cm. - Socio-affective computing,v.82509-5706 ;. - Socio-affective computing ;v.1..

Preface -- 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.

This 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.

ISBN: 9783319950204$q(electronic bk.)

Standard No.: 10.1007/978-3-319-95020-4doiSubjects--Topical Terms:

210824
Computational intelligence.

LC Class. No.: Q342 / .P675 2018

Dewey Class. No.: 006.3

Multimodal sentiment analysis
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245 1 0 $a Multimodal sentiment analysis $h [electronic resource] / $c by Soujanya Poria, Amir Hussain, Erik Cambria.
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490 1 $a Socio-affective computing, $x 2509-5706 ; $v v.8
505 0 $a Preface -- 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 $a This 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.
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