Buch, Englisch, Band 8, 214 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 553 g
Reihe: Socio-Affective Computing
Buch, Englisch, Band 8, 214 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 553 g
Reihe: Socio-Affective Computing
ISBN: 978-3-319-95018-1
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Multimedia
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
- Geisteswissenschaften Sprachwissenschaft Übersetzungswissenschaft, Translatologie, Dolmetschen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
Weitere Infos & Material
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.