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Multimodal Sentiment Analysis


Multimodal Sentiment Analysis
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Beschreibung

Preface 

1 Introduction and Motivation
Research Challenges in Text-Based Sentiment Analysis
Research Challenges in Multimodal Sentiment Analysis
Overview of the Proposed Framework
Contributions of this Book
Book Organisation

 2 Background
Affective Computing
Sentiment Analysis
Pattern Recognition  
Feature Selection
Model Evaluation Techniques  
Model Validation Techniques
Classification Techniques
Feature-Based Text Representation
Conclusion

 3 Literature Survey and Datasets
Introduction
Available Datasets
Visual, Audio Features for Affect Recognition
Multimodal Affect Recognition
Available APIs
Discussion
Conclusion

 4 Concept Extraction from Natural Text for Concept Level Text Analysis
Introduction
The patterns for concept extraction
Experiments and Results
Conclusion

 5 EmoSenticSpace: Dense concept-based affective features with common-sense knowledge
Introduction
Lexical Resources Used
Features Used for Classification
Fuzzy Clustering
Hard Clustering
Implementation
Direct Evaluation Of The Assigned Emotion Labels
Construction Of Emosenticspace
Performance on Applications
Summary of Lexical Resources and Features Used
Conclusion

 6 Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns
Introduction
General rules
Combining sentic patterns with machine learning for text-based
sentiment analysis
Evaluation
Conclusion 

7 Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis
Introduction
Extracting Features from Textual Data
Extracting Features from Visual Data
Extracting Features from Audio Data
Experimental Results
Speeding up the computational time: The role of ELM
Improved multimodal sentiment analysis: Deep learning-based
visual feature extraction
Convolutional Recurrent Multiple Kernel Learning (CRMKL)
Experimental Results and Discussion
Conclusion 

8 Conclusion and Future Work
Social Impact
Advantages
Limitations
Future Work

Index

Eigenschaften

Breite: 156
Gewicht: 520 g
Höhe: 251
Länge: 17
Seiten: 214
Sprachen: Englisch
Autor: Amir Hussain, Erik Cambria, Soujanya Poria

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