Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks
This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated fe
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Arindam Chaudhuri
Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks 123
SpringerBriefs in Computer Science Series Editors Stan Zdonik, Brown University, Providence, RI, USA Shashi Shekhar, University of Minnesota, Minneapolis, MN, USA Xindong Wu, University of Vermont, Burlington, VT, USA Lakhmi C. Jain, University of South Australia, Adelaide, SA, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, IL, USA Xuemin Sherman Shen, University of Waterloo, Waterloo, ON, Canada Borko Furht, Florida Atlantic University, Boca Raton, FL, USA V. S. Subrahmanian, University of Maryland, College Park, MD, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, PA, USA Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, VA, USA Newton Lee, Institute for Education Research and Scholarships, Los Angeles, CA, USA
SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical topics might include: • A timely report of state-of-the art analytical techniques • A bridge between new research results, as published in journal articles, and a contextual literature review • A snapshot of a hot or emerging topic • An in-depth case study or clinical example • A presentation of core concepts that students must understand in order to make independent contributions Briefs allow authors to present their ideas and readers to absorb them with minimal time investment. Briefs will be published as part of Springer’s eBook collection, with millions of users worldwide. In addition, Briefs will be available for individual print and electronic purchase. Briefs are characterized by fast, global electronic dissemination, standard publishing contracts, easy-to-use manuscript preparation and formatting guidelines, and expedited production schedules. We aim for publication 8–12 weeks after acceptance. Both solicited and unsolicited manuscripts are considered for publication in this series.
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Arindam Chaudhuri
Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks
123
Arindam Chaudhuri Samsung R & D Institute Delhi Noida, India
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-981-13-7473-9 ISBN 978-981-13-7474-6 (eBook) https://doi.org/10.1007/978-981-13-7474-6 Library of Congress Control Number: 2019935836 © The Author(s), under exclusive to Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any
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