Main description:

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.

Contents:

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