Advanced Deep Learning for Text and Image Sentiment Analysis
Language: English
Published by Pippet Sky, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 56.92
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Advanced Deep Learning for Text and Image Sentiment Analysis presents a focused technical treatment of deep learning methods for sentiment analysis across textual and visual data. The book examines how neural network architectures can extract meaningful patterns from language and images to support automated sentiment classification. It introduces core concepts in deep learning, text representation, image feature extraction, multimodal analysis, and sentiment modeling, with attention to computational challenges in processing heterogeneous data. The discussion connects natural language processing and computer vision with machine-learning approaches used to identify sentiment from written and visual content. Topics include feature learning, representation learning, neural-network classification, text sentiment analysis, image sentiment analysis, and evaluation for prediction systems. It provides references for readers on deep-learning techniques for sentiment analysis and multimodal data.…
Seller Inventory # 9798240866654
- Title
- Advanced Deep Learning for Text and Image Sentiment Analysis
- Author
- Morenski
- Publisher
- Pippet Sky
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798240866654
- Item weight
- 240 grams
- Dimensions
- 229x152x9 mm
Advanced Deep Learning for Text and Image Sentiment Analysis presents a focused technical treatment of deep learning methods for sentiment analysis across textual and visual data. The book examines how neural network architectures can extract meaningful patterns from language and images to support automated sentiment classification. It introduces core concepts in deep learning, text representation, image feature extraction, multimodal analysis, and sentiment modeling, with attention to computational challenges in processing heterogeneous data. The discussion connects natural language processing and computer vision with machine-learning approaches used to identify sentiment from written and visual content. Topics include feature learning, representation learning, neural-network classification, text sentiment analysis, image sentiment analysis, and evaluation for prediction systems. It provides references for readers on deep-learning techniques for sentiment analysis and multimodal data.
"Synopsis" may belong to another edition of this title.
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