Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks PDF Download
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Author: Arindam Chaudhuri
Publisher: Springer
ISBN: 9811374740
Category : Computers
Languages : en
Pages : 109
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Book Description
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 feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
Author: Arindam Chaudhuri
Publisher: Springer
ISBN: 9811374740
Category : Computers
Languages : en
Pages : 109
Get Book Here
Book Description
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 feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
Author:
Publisher: BoD – Books on Demand
ISBN: 1803569506
Category : Computers
Languages : en
Pages : 132
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Book Description
Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.
Author: Yi Pan
Publisher: Springer Nature
ISBN: 3030960331
Category : Computers
Languages : en
Pages : 123
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Book Description
This book constitutes the proceedings of the 10th International Conference on Artificial Intelligence and Mobile Services, AIMS 2021, held as a virtual conference as part of SCF 2021, during December 10-14, 2021. The 9 full presented were carefully reviewed and selected from 20 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, mobile backend as a service (MBaaS), user experience of AI and mobile services.
Author: Aboul Ella Hassanien
Publisher: Springer Nature
ISBN: 9813343672
Category : Technology & Engineering
Languages : en
Pages : 922
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Book Description
This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of things (IoT) and information security.
Author: Jagdish Chand Bansal
Publisher: Springer Nature
ISBN: 9819765889
Category :
Languages : en
Pages : 510
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Book Description
Author: Yizhang Jiang
Publisher: Frontiers Media SA
ISBN: 2832505171
Category : Science
Languages : en
Pages : 413
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Book Description
Author: Xiao Bai
Publisher: Frontiers Media SA
ISBN: 2832522696
Category : Science
Languages : en
Pages : 155
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Book Description
Author: Hua Xu
Publisher: Springer Nature
ISBN: 9819957761
Category : Technology & Engineering
Languages : en
Pages : 278
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Book Description
The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.
Author: Rajest, S. Suman
Publisher: IGI Global
ISBN: 1668491915
Category : Computers
Languages : en
Pages : 383
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Book Description
Establishing the means to improve performance in healthy, clinical, and military populations has long been a focus of study in the psychological and brain sciences. However, a major obstacle to this goal is generating individualized performance phenotypes that allow for the design of interventions that are tailored to the specific needs of the individual. Recent developments in artificial intelligence (AI) have qualified for the development of precision approaches that consider individual differences, allowing, for example, the establishment of individualized training, preparation, and recuperation programs optimal for an individual’s cognitive and biological phenotype. Corollary developments in AI have proven that combining domain expertise and stakeholder insights can considerably improve AI’s quality, performance, and dependability in the psychology and brain sciences. Recent Developments in Machine and Human Intelligence studies original empirical work, literature reviews, and methodological papers that establish and validate precision AI methods for human performance optimization with a focus on modeling individual differences via state-of-the-art computational methods and investigating how domain expertise and human judgment can improve the performance of AI methods. The topics are crafted in such a way as to cover all the areas of artificial and human intelligence that require AI for further development. This book contains algorithms and techniques that are explained with the help of developed source code and encompasses the readiness and needs for advancements in managing yet another pandemic in the future. It is designed for academicians, scientists, research scholars, professors, graduates, undergraduates, and students.
Author: Jyotsna Kumar Mandal
Publisher: Springer Nature
ISBN: 9811652074
Category : Technology & Engineering
Languages : en
Pages : 837
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Book Description
This book gathers selected high-quality research papers presented at the 2nd International Conference on Advanced Computing Applications (ICACA 2021), held virtually during 27––28 March 2021. The book is divided into four sections. These are communication and computing, signal processing and multimedia, computational intelligence and data analytics and decision computing. The topics covered are advanced communication technologies, IoT-based systems and applications, network security and reliability, virtualization technologies, compressed sensors and multimedia applications, signal image and video processing, machine learning, pattern recognitions, intelligent computing, big data analytics, analytics in bio-computing, AI-driven 6G mobile wireless networks and autonomous driving.
