“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)

• Preface.- Introduction and Motivation.- Background.- Literature Survey and Datasets.- Concept Extraction from Natural Text for Concept Level Text Analysis.- EmoSenticSpace: Dense concept-based affective features with common-sense knowledge.- Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns.- Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis.- Conclusion and Future Work.- Index.

“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)

• Preface.- Introduction and Motivation.- Background.- Literature Survey and Datasets.- Concept Extraction from Natural Text for Concept Level Text Analysis.- EmoSenticSpace: Dense concept-based affective features with common-sense knowledge.- Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns.- Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis.- Conclusion and Future Work.- Index.

“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)

• Preface.- Introduction and Motivation.- Background.- Literature Survey and Datasets.- Concept Extraction from Natural Text for Concept Level Text Analysis.- EmoSenticSpace: Dense concept-based affective features with common-sense knowledge.- Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns.- Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis.- Conclusion and Future Work.- Index.