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This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Book Synopsis Deep Learning-Based Approaches for Sentiment Analysis by : Basant Agarwal
Download or read book Deep Learning-Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Book Synopsis Deep Learning-Based Approaches for Sentiment Analysis by : Basant Agarwal
Download or read book Deep Learning-Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer. This book was released on 2021-01-25 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Book Synopsis Deep Learning-based Approaches for Sentiment Analysis by :
Download or read book Deep Learning-based Approaches for Sentiment Analysis written by and published by . This book was released on 2020 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Big data generates around us constantly from daily business, custom use, engineering, and science activities. Sensory data is collected from the internet of things (IoT) and cyber-physical systems (CPS). Merely storing such a massive amount of data is meaningless, as the key point is to identify, locate, and extract valuable knowledge from big data to forecast and support services. Such extracted valuable knowledge is usually referred to as smart data. It is vital to providing suitable decisions in business, science, and engineering applications. Deep Learning Applications for Cyber-Physical Systems provides researchers a platform to present state-of-the-art innovations, research, and designs while implementing methodological and algorithmic solutions to data processing problems and designing and analyzing evolving trends in health informatics and computer-aided diagnosis in deep learning techniques in context with cyber physical systems. Covering topics such as smart medical systems, intrusion detection systems, and predictive analytics, this text is essential for computer scientists, engineers, practitioners, researchers, students, and academicians, especially those interested in the areas of internet of things, machine learning, deep learning, and cyber-physical systems.
Book Synopsis Deep Learning Applications for Cyber-Physical Systems by : Mundada, Monica R.
Download or read book Deep Learning Applications for Cyber-Physical Systems written by Mundada, Monica R. and published by IGI Global. This book was released on 2021-12-17 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data generates around us constantly from daily business, custom use, engineering, and science activities. Sensory data is collected from the internet of things (IoT) and cyber-physical systems (CPS). Merely storing such a massive amount of data is meaningless, as the key point is to identify, locate, and extract valuable knowledge from big data to forecast and support services. Such extracted valuable knowledge is usually referred to as smart data. It is vital to providing suitable decisions in business, science, and engineering applications. Deep Learning Applications for Cyber-Physical Systems provides researchers a platform to present state-of-the-art innovations, research, and designs while implementing methodological and algorithmic solutions to data processing problems and designing and analyzing evolving trends in health informatics and computer-aided diagnosis in deep learning techniques in context with cyber physical systems. Covering topics such as smart medical systems, intrusion detection systems, and predictive analytics, this text is essential for computer scientists, engineers, practitioners, researchers, students, and academicians, especially those interested in the areas of internet of things, machine learning, deep learning, and cyber-physical systems.
This book features selected papers presented at the 2nd International Conference on Advanced Computing Technologies and Applications, held at SVKM’s Dwarkadas J. Sanghvi College of Engineering, Mumbai, India, from 28 to 29 February 2020. Covering recent advances in next-generation computing, the book focuses on recent developments in intelligent computing, such as linguistic computing, statistical computing, data computing and ambient applications.
Book Synopsis Advanced Computing Technologies and Applications by : Hari Vasudevan
Download or read book Advanced Computing Technologies and Applications written by Hari Vasudevan and published by Springer Nature. This book was released on 2020-05-06 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected papers presented at the 2nd International Conference on Advanced Computing Technologies and Applications, held at SVKM’s Dwarkadas J. Sanghvi College of Engineering, Mumbai, India, from 28 to 29 February 2020. Covering recent advances in next-generation computing, the book focuses on recent developments in intelligent computing, such as linguistic computing, statistical computing, data computing and ambient applications.
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Book Synopsis Supervised Machine Learning for Text Analysis in R by : Emil Hvitfeldt
Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
From past decades, Computational Intelligence CI encompasses a wide range of computational methodologies, which mainly includes neural networks, Fuzzy Systems, Genetic algorithms and other such hybrid computing models to address various real world complexities and uncertainties Recently, the emerging intelligent computing technologies focus primarily on solving the data analysis challenges in various real time applications like industries, financial and business models, scientific and social networking applications The International Conference on Inventive Computation technologies ICICT 2021 organized by RVS Technical Campus on 20 22 January, 2021 attempts to create a collaborative research platform to foster innovative research insights in the design, development, and applications of intelligent computing technologies
Book Synopsis 2021 6th International Conference on Inventive Computation Technologies (ICICT) by : IEEE Staff
Download or read book 2021 6th International Conference on Inventive Computation Technologies (ICICT) written by IEEE Staff and published by . This book was released on 2021-01-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: From past decades, Computational Intelligence CI encompasses a wide range of computational methodologies, which mainly includes neural networks, Fuzzy Systems, Genetic algorithms and other such hybrid computing models to address various real world complexities and uncertainties Recently, the emerging intelligent computing technologies focus primarily on solving the data analysis challenges in various real time applications like industries, financial and business models, scientific and social networking applications The International Conference on Inventive Computation technologies ICICT 2021 organized by RVS Technical Campus on 20 22 January, 2021 attempts to create a collaborative research platform to foster innovative research insights in the design, development, and applications of intelligent computing technologies
Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification.
Book Synopsis Comparison of neutrosophic approach to various deep learning models for sentiment analysis by : Mayukh Sharma
Download or read book Comparison of neutrosophic approach to various deep learning models for sentiment analysis written by Mayukh Sharma and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification.
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
Book Synopsis Sentiment Analysis by : Bing Liu
Download or read book Sentiment Analysis written by Bing Liu and published by Cambridge University Press. This book was released on 2020-10-15 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.
Book Synopsis Sentiment Analysis for Social Media by : Carlos A. Iglesias
Download or read book Sentiment Analysis for Social Media written by Carlos A. Iglesias and published by MDPI. This book was released on 2020-04-02 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.
