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Multimodal Sentiment Analysis

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  • © 2018

Overview

Authors:
  1. Soujanya Poria

    1. Artificial Intelligence Initiative, ASTAR, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  2. Amir Hussain

    1. Computing Science and Mathematics, University of Stirling, Stirling, UK

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    You can also search for this author in PubMed   Google Scholar

  3. Erik Cambria

    1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  • Broadens understanding of multimodal sentiment analysis
  • Presents a summary of the relevant state of the art
  • Contains key visualizations

Part of the book series: Socio-Affective Computing (SAC, volume 8)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction and Motivation

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 1-12

  3. Background

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 13-35

  4. Literature Survey and Datasets

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 37-78

  5. Concept Extraction from Natural Text for Concept Level Text Analysis

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 79-84

  6. EmoSenticSpace: Dense Concept-Based Affective Features with Common-Sense Knowledge

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 85-116

  7. Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 117-151

  8. Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 153-178

  9. Conclusion and Future Work

    • Soujanya Poria, Amir Hussain, Erik Cambria
    Pages 179-186

  10. Back Matter

    Pages 187-214
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Keywords

About this book

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.

Reviews

“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)

Authors and Affiliations

  • Artificial Intelligence Initiative, ASTAR, Singapore, Singapore

    Soujanya Poria

  • Computing Science and Mathematics, University of Stirling, Stirling, UK

    Amir Hussain

  • School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

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