Multimodal Sentiment Analysis: Socio-Affective Computing, cartea 8
Autor Soujanya Poria, Amir Hussain, Erik Cambriaen Limba Engleză Paperback – 28 dec 2018
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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| Springer International Publishing – 28 dec 2018 | 842.32 lei 38-44 zile | |
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Specificații
ISBN-13: 9783030069568
ISBN-10: 3030069567
Pagini: 214
Ilustrații: XI, 214 p. 34 illus., 25 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:Softcover reprint of the original 1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Socio-Affective Computing
Locul publicării:Cham, Switzerland
ISBN-10: 3030069567
Pagini: 214
Ilustrații: XI, 214 p. 34 illus., 25 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:Softcover reprint of the original 1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Socio-Affective Computing
Locul publicării:Cham, Switzerland
Cuprins
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.
Recenzii
“I consider the book a useful resource for various audiences interested in the topic of multimodal sentiment analysis. It offers a thorough review of the state of the art and important domain concepts, and includes considerable contributions by the authors toward various aspects of the discussed topics.” (M. Bielikova, Computing Reviews, August 9, 2021)
Textul de pe ultima copertă
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.
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.
Caracteristici
Broadens understanding of multimodal sentiment analysis Presents a summary of the relevant state of the art Contains key visualizations