Multi Modal Sentiment Analysis PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Multi Modal Sentiment Analysis PDF full book. Access full book title Multi Modal Sentiment Analysis.
Author | : Hua Xu |
Publisher | : Springer Nature |
Total Pages | : 278 |
Release | : 2023-11-26 |
Genre | : Technology & Engineering |
ISBN | : 9819957761 |
Download Multi-Modal Sentiment Analysis Book in PDF, ePub and Kindle
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 | : Soujanya Poria |
Publisher | : Springer |
Total Pages | : 214 |
Release | : 2018-10-24 |
Genre | : Medical |
ISBN | : 3319950207 |
Download Multimodal Sentiment Analysis Book in PDF, ePub and Kindle
This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. 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.
Author | : Soujanya Poria |
Publisher | : |
Total Pages | : 214 |
Release | : 2018 |
Genre | : COMPUTERS |
ISBN | : 9783319950198 |
Download Multimodal Sentiment Analysis Book in PDF, ePub and Kindle
This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. 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.
Author | : Hua Xu |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2023-11-26 |
Genre | : Technology & Engineering |
ISBN | : 9789819957750 |
Download Multi-Modal Sentiment Analysis Book in PDF, ePub and Kindle
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 | : Lukas Bernd Stappen |
Publisher | : |
Total Pages | : 0 |
Release | : 2021 |
Genre | : |
ISBN | : |
Download Multimodal Sentiment Analysis in Real-life Videos Book in PDF, ePub and Kindle
Author | : Pradeep Kumar Singh |
Publisher | : Springer Nature |
Total Pages | : 846 |
Release | : 2021-01-12 |
Genre | : Technology & Engineering |
ISBN | : 9811582971 |
Download Recent Innovations in Computing Book in PDF, ePub and Kindle
This book features selected papers presented at the 3rd International Conference on Recent Innovations in Computing (ICRIC 2020), held on 20–21 March 2020 at the Central University of Jammu, India, and organized by the university’s Department of Computer Science & Information Technology. It includes the latest research in the areas of software engineering, cloud computing, computer networks and Internet technologies, artificial intelligence, information security, database and distributed computing, and digital India.
Author | : Kah Phooi Seng |
Publisher | : Springer |
Total Pages | : 391 |
Release | : 2019-07-18 |
Genre | : Computers |
ISBN | : 3319975986 |
Download Multimodal Analytics for Next-Generation Big Data Technologies and Applications Book in PDF, ePub and Kindle
This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
Author | : Xavier Alameda-Pineda |
Publisher | : Academic Press |
Total Pages | : 498 |
Release | : 2018-11-13 |
Genre | : Computers |
ISBN | : 0128146028 |
Download Multimodal Behavior Analysis in the Wild Book in PDF, ePub and Kindle
Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing. Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
Author | : Erik Cambria |
Publisher | : Springer Science & Business Media |
Total Pages | : 166 |
Release | : 2012-07-28 |
Genre | : Medical |
ISBN | : 9400750706 |
Download Sentic Computing Book in PDF, ePub and Kindle
In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
Author | : Arindam Chaudhuri |
Publisher | : Springer |
Total Pages | : 98 |
Release | : 2019-04-06 |
Genre | : Computers |
ISBN | : 9811374740 |
Download Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks Book in PDF, ePub and Kindle
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.