Multimodal Sentiment Analysis

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160,49 

Socio-Affective Computing 8

ISBN: 3319950185
ISBN 13: 9783319950181
Autor: Poria, Soujanya/Hussain, Amir/Cambria, Erik
Verlag: Springer Verlag GmbH
Umfang: xi, 214 S., 9 s/w Illustr., 25 farbige Illustr., 214 p. 34 illus., 25 illus. in color.
Erscheinungsdatum: 03.11.2018
Auflage: 1/2018
Produktform: Gebunden/Hardback
Einband: Gebunden

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.

Artikelnummer: 5202319 Kategorie:

Beschreibung

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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E-Mail: juergen.hartmann@springer.com

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