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.

Contenu

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