Multimodal sentiment analysis. (2018)
- Record Type:
- Book
- Title:
- Multimodal sentiment analysis. (2018)
- Main Title:
- Multimodal sentiment analysis
- Further Information:
- Note: Soujanya Poria, Amir Hussain, Erik Cambria.
- Authors:
- Poria, Soujanya
Hussain, A (Amir)
Cambria, Erik - Contents:
- Intro; Preface; Contents; 1 Introduction and Motivation; 1.1 Research Challenges in Text-Based Sentiment Analysis; 1.2 Research Challenges in Multimodal Sentiment Analysis; 1.3 Overview of the Proposed Framework; 1.3.1 Text-Based Sentiment Analysis; 1.3.2 Multimodal Sentiment Analysis; 1.4 Contributions of This Book; 1.4.1 Text-Based Sentiment Analysis; 1.4.2 Multimodal Sentiment Analysis; 1.5 Book Organisation; 2 Background; 2.1 Affective Computing; 2.2 Sentiment Analysis; 2.2.1 Opinion Holder; 2.2.2 Aspects; 2.2.3 Subjectivity; 2.3 Pattern Recognition; 2.3.1 Features; 2.3.2 Pattern 2.3.3 Class2.4 Feature Selection; 2.4.1 Principal Component Analysis; 2.5 Model Evaluation Techniques; 2.5.1 Evaluating Regression Quality; 2.5.1.1 Mean Squared Error Validation Techniques; 2.5.2 Evaluating Classification Techniques; 2.5.2.1 Precision; 2.5.2.2 Recall; 2.5.2.3 F-Score; 2.5.2.4 Accuracy; 2.6 Model Validation Techniques; 2.6.1 Cross Validation; 2.6.2 Bootstrapping; 2.7 Classification Techniques; 2.7.1 Support Vector Machine; 2.7.1.1 Soft-Margin Extension; 2.7.1.2 Non-linear Decision Boundary; 2.7.1.3 Formulation as a Lagrangian Optimization; 2.7.1.4 Kernel Trick 2.7.2 Extreme Learning Machine2.7.3 Deep Neural Networks; 2.8 Feature-Based Text Representation; 2.8.1 Types of Feature-Based Text Representation; 2.8.1.1 One-Hot Vectors; 2.8.1.2 Distributional Vectors; 2.8.1.3 Word Embeddings; 2.8.2 Word2Vec; 2.8.2.1 Negative Sampling; 2.8.2.2 Classification Problem; 2.9 Conclusion; 3Intro; Preface; Contents; 1 Introduction and Motivation; 1.1 Research Challenges in Text-Based Sentiment Analysis; 1.2 Research Challenges in Multimodal Sentiment Analysis; 1.3 Overview of the Proposed Framework; 1.3.1 Text-Based Sentiment Analysis; 1.3.2 Multimodal Sentiment Analysis; 1.4 Contributions of This Book; 1.4.1 Text-Based Sentiment Analysis; 1.4.2 Multimodal Sentiment Analysis; 1.5 Book Organisation; 2 Background; 2.1 Affective Computing; 2.2 Sentiment Analysis; 2.2.1 Opinion Holder; 2.2.2 Aspects; 2.2.3 Subjectivity; 2.3 Pattern Recognition; 2.3.1 Features; 2.3.2 Pattern 2.3.3 Class2.4 Feature Selection; 2.4.1 Principal Component Analysis; 2.5 Model Evaluation Techniques; 2.5.1 Evaluating Regression Quality; 2.5.1.1 Mean Squared Error Validation Techniques; 2.5.2 Evaluating Classification Techniques; 2.5.2.1 Precision; 2.5.2.2 Recall; 2.5.2.3 F-Score; 2.5.2.4 Accuracy; 2.6 Model Validation Techniques; 2.6.1 Cross Validation; 2.6.2 Bootstrapping; 2.7 Classification Techniques; 2.7.1 Support Vector Machine; 2.7.1.1 Soft-Margin Extension; 2.7.1.2 Non-linear Decision Boundary; 2.7.1.3 Formulation as a Lagrangian Optimization; 2.7.1.4 Kernel Trick 2.7.2 Extreme Learning Machine2.7.3 Deep Neural Networks; 2.8 Feature-Based Text Representation; 2.8.1 Types of Feature-Based Text Representation; 2.8.1.1 One-Hot Vectors; 2.8.1.2 Distributional Vectors; 2.8.1.3 Word Embeddings; 2.8.2 Word2Vec; 2.8.2.1 Negative Sampling; 2.8.2.2 Classification Problem; 2.9 Conclusion; 3 Literature Survey and Datasets; 3.1 Introduction; 3.2 Available Datasets; 3.2.1 Datasets for Multimodal Sentiment Analysis; 3.2.1.1 YouTube Dataset; 3.2.1.2 MOUD Dataset; 3.2.1.3 ICT-MMMO Database; 3.2.2 Datasets for Multimodal Emotion Recognition; 3.2.2.1 HUMAINE Database 3.2.2.2 The Belfast Database3.2.2.3 The SEMAINE Database; 3.2.2.4 Interactive Emotional Dyadic Motion Capture Database (IEMOCAP); 3.2.2.5 The eNTERFACE Database; 3.2.3 Affective Detection from Textual Modality; 3.2.3.1 Single- vs. Cross-Domain; 3.2.3.2 Use of Linguistic Patterns; 3.2.3.3 Bag of Words Versus Bag of Concepts; 3.2.3.4 Contextual Subjectivity; 3.2.3.5 New Era of NLP: Emergence of Deep Learning; 3.3 Visual, Audio Features for Affect Recognition; 3.3.1 Visual Modality; 3.3.1.1 Facial Action Coding System; 3.3.1.2 Main Facial Expression Recognition Techniques 3.3.1.3 Extracting Temporal Features from Videos3.3.1.4 Body Gestures; 3.3.1.5 New Era: Deep Learning to Extract Visual Features; 3.3.2 Audio Modality; 3.3.2.1 Local Features vs. Global Features; 3.3.2.2 Speaker-Independent Applications; 3.3.2.3 Audio Features Extraction Using Deep Networks; 3.4 Multimodal Affect Recognition; 3.4.1 Information Fusion Techniques; 3.4.1.1 Decision-Level or Late Fusion alam2014predicting, cai2015convolutional, yamasaki2015prediction, glodek2013kalman, dobrivsek2013towards; 3.4.1.2 Hybrid Multimodal Fusion wollmer2013YouTube, mansoorizadeh2010multimodal … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2018
- Extent:
- 1 online resource (xi, 214 pages), illustrations (some color)
- Subjects:
- 006.3
Medicine
Computational intelligence
Human-computer interaction
Computers -- Interactive & Multimedia
Computers -- Computer Graphics
Computers -- Natural Language Processing
Language Arts & Disciplines -- Translating & Interpreting
Graphical & digital media applications
Image processing
Natural language & machine translation
Translation & interpretation
Neurosciences
Multimedia systems
Computer vision
Translators (Computer programs)
Translating and interpreting
Medical -- Neuroscience
Neurosciences
Electronic books - Languages:
- English
- ISBNs:
- 9783319950204
3319950207 - Related ISBNs:
- 9783319950181
3319950185 - Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (SpringerLink, viewed November 2, 2018). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.347537
- Ingest File:
- 01_301.xml