Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics
Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Convergent Journalism is an online news system that uses a range of media and methods to collect and present information. With the advent and growth of the Internet, this form of news has been flourishing globally and has become the mainstream in China. In 2014, the Chinese Government established media convergence as a national strategy. This book offers a panoramic view of the theories and practice of Convergent Journalism in a Chinese media landscape. Drawing on a plethora of cases, the author introduces concepts, subjects, and processes, and elaborates on media components including text, visuals, audio, and video. In addition, he discusses the application of search engine optimization, hyperlinks in reporting, user interaction, and user creation of content. Aside from providing an in-depth theoretical analysis, the book provides much guidance for practitioners. Students, scholars, and professionals of communication studies, journalism, and media studies will benefit from this book.
By quoting celebrities’ wonderful remarks, the author discusses systematically the art, essence, function and development of calligraphy as well as how to learn, write and appreciate calligraphy. The book deals with artistic conception and style of calligraphy and reveals similarities among calligraphy, painting, music, dance and architecture. The author regards that the essence of calligraphy is beauty and elaborates how to write calligraphy. The thought-provoking material has great value for the contribution toward Chinese culture and also proves an extremely useful resource for anyone dedicated to calligraphy. The work is so unique and insightful to read.
Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
Convergent journalism is an online news system that uses a range of media and methods to collect and present information. With the advent and growth of the Internet, this form of news has been flourishing globally and has become the mainstream in China. In 2014, the Chinese government established media convergence as a national strategy. This book offers a panoramic view of the theories and practice of convergent journalism in the Chinese media landscape. Drawing from a plethora of cases, the author introduces concepts, subjects and processes, and elaborates on media components including text, visuals, audio, and video. In addition, he discusses the application of search engine optimization, hyperlinks in reporting, user interaction and user creation of content. Aside from providing an in-depth theoretical analysis, the book provides much guidance for practitioners. Students, scholars and professionals of communication studies, journalism, and media studies will benefit from this book"--
Visual perception and its relationship to the subsequent manipulative behaviors are fundamental for people to recognize the world. The most important manipulations of drivers are speed control and steering, which could possibly guarantee a safe driving. So, to avoid accidents the driver does a series of judgments, decisions and actions, which could be impact by the visual information it perceived. Over the past few decades, visual perception has gradually gone from the psychology domain to its relevant fields, like the transportation science and engineering, to play a more important role in the human factors in transportation. This books presents the state of the art in speed perception and its application with a kind of edge rate markings installed on roadways with empirical on-road experiments and field observations of naturalistic driving data. Reaching a great combination of the fundamental theory in cognitive psychology and the issues in traffic and transportation engineering, this books is one of the most practical and up-to-date references available on the subject of influence of visual perception on drivers’ speed control and steering behaviors. This allows the knowledge of visual perception and transportation accessible to a wider range of audiences, and also introduce new thoughts and new methods for decision-makers, practitioners in dealing with traffic safety or related issues. The fundamental concepts, experimental process, statistical analyses, and comprehensive discussions are covered in detail, providing the readers a systematic understanding of the field.
This book provides interpretation of China’s logistics development in a new development paradigm and the rural logistics construction under the Rural Revitalization Strategy. Subjects covered in this book encompass the macro-factors pertaining to the overall development in logistics technologies and facilities, region-specific policies and plans, industry-wide transformation in transport, manufacturing, commerce and agriculture. Specifically, this book highlights the impact of COVID-19 on China’s logistics industry, and demonstrates the efforts and contributions of China’s logistics in the fight against COVID-19 in 2020. Aligns with the previous volumes, the ultimate aim of this book is to present a timely portrait of the rapid growth of China’s logistics market and the status quo of its logistics industry. In so doing, the book offers an in-depth analysis of critical issues involved in the ongoing dynamic and multi-faceted development and provides a valuable reference resource for interested readers in the academic and professional fields.
This book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research.
In this book, the authors cover the recent progress in the synthesis, characterization and application of various multi-layered carbides, carbonitrides and nitrides. Moreover, the processing and development of MXene-based composites are elaborated, focusing on their applications and performances as transparent conductors in environmental remediation and energy storage systems.
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