Bücher von Bing Liu

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.

Dans Fednet, il existe deux approches pour fournir les services. L'une est superposée et l'autre est basée sur un proxy. Chacune de ces approches présente des avantages et des inconvénients. Afin de trouver un compromis entre ces deux approches, nous proposons un nouveau système qui peut rendre le mode de fourniture des services dans Fednet flexible et adaptable à l'environnement changeant et aux préférences de l'utilisateur. Certains modules sont conçus dans l'agent Fednet (FA), le gestionnaire Fednet (FM) et le proxy de service pour soutenir notre nouveau mécanisme. Pour illustrer pleinement le concept de Fednet et la fourniture de services dans Fednet, nous mettons en ¿uvre un prototype de Fednet. Il nous montre comment le Fednet est formé et comment les messages sont échangés entre l'agent FA et le gestionnaire de Fednet. Dans le but de prouver que nos algorithmes de prise de décision peuvent prendre des décisions en fonction des besoins de l'utilisateur et de l'évolution du contexte, certaines simulations et une expérience réelle sur banc d'essai basée sur un ou deux paramètres sont réalisées. Les résultats des simulations montrent que notre nouveau mécanisme de fourniture de services pourrait rendre le mode de fourniture de services flexible et adaptable comme nous l'avons prévu.

W Fednecie istniej¿ dwa podej¿cia do ¿wiadczenia us¿ug. Jedno z nich jest nak¿adane, a drugie oparte jest na proxy. Käde z tych podej¿¿ ma swoje zalety i wady. Aby dokonä kompromisu mi¿dzy tymi dwoma podej¿ciami, proponujemy nowy schemat, który mo¿e uczyni¿ sposób dostarczania us¿ug w Fednecie elastycznym i dostosowanym do zmieniaj¿cego si¿ ¿rodowiska i preferencji u¿ytkownika. Niektóre modu¿y s¿ zaprojektowane w agencie Fednet (FA), mened¿erze Fednet (FM) i serwerze proxy do obs¿ugi naszego nowego mechanizmu. Aby w pe¿ni zilustrowä koncepcj¿ Fednetu i ¿wiadczenie us¿ug w Fednecie, implementujemy prototyp Fednetu. Pokazuje on nam, jak Fednet jest tworzony i jak wiadomo¿ci s¿ wymieniane mi¿dzy FA i FM. W celu udowodnienia, ¿e nasze algorytmy decyzyjne mog¿ podejmowä decyzje zgodnie z wymaganiami u¿ytkownika i zmieniaj¿cym si¿ kontekstem, przeprowadzane s¿ pewne symulacje i prawdziwy eksperyment w ¿ó¿ku testowym oparty na jednym lub dwóch parametrach. Wyniki symulacji pokazuj¿, ¿e nasz nowy mechanizm ¿wiadczenia us¿ug mo¿e sprawi¿, ¿e sposób ¿wiadczenia us¿ug b¿dzie elastyczny i dostosowany do naszych oczekiwä.

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).

Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.

Now in its second, updated edition, this authoritative and coherent text contains a rich blend of theory and practice and covers all the essential concepts and algorithms from relevant fields such as data mining, machine learning, and text processing.