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"This book provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, it will provide libraries with the defining reference on this topic"--Provided by publisher.
Book Synopsis Emerging Technologies of Text Mining: Techniques and Applications by : do Prado, Hercules Antonio
Download or read book Emerging Technologies of Text Mining: Techniques and Applications written by do Prado, Hercules Antonio and published by IGI Global. This book was released on 2007-10-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, it will provide libraries with the defining reference on this topic"--Provided by publisher.
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
Book Synopsis Survey of Text Mining by : Michael W. Berry
Download or read book Survey of Text Mining written by Michael W. Berry and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
What Is Text Mining Text mining, also known as text data mining (TDM) or text analytics, is the technique of extracting useful information from text. Related terms include text data mining (TDM) and text analytics. It is "the discovery by computer of new, previously unknown information by automatically extracting information from various written resources," according to one definition of the term. Websites, books, emails, reviews, and articles are all examples of written materials that may be utilized. Typically, the best way to acquire high-quality information is to construct patterns and trends through the use of methods such as statistical pattern learning. According to Hotho et al. (2005), we are able to differentiate between three distinct perspectives of text mining. These perspectives are information extraction, data mining, and a process known as knowledge discovery in databases (KDD). Text mining often entails the process of structuring the text that is input, determining patterns within the data that has been structured, and then lastly evaluating and interpreting the result of the mining process. When discussing text mining, the term "high quality" typically relates to some combination of the concepts of relevance, novelty, and interest. Text categorization, text clustering, concept/entity extraction, generation of granular taxonomies, sentiment analysis, document summarizing, and entity relation modeling are all examples of typical text mining activities. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Text Mining Chapter 2: Natural Language Processing Chapter 3: Data Mining Chapter 4: Information Extraction Chapter 5: Semantic Similarity Chapter 6: Unstructured Data Chapter 7: Biomedical Text Mining Chapter 8: Sentiment Analysis Chapter 9: Word Embedding Chapter 10: Social Media Mining (II) Answering the public top questions about text mining. (III) Real world examples for the usage of text mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of text mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of text mining.
Book Synopsis Text Mining by : Fouad Sabry
Download or read book Text Mining written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-05 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Text Mining Text mining, also known as text data mining (TDM) or text analytics, is the technique of extracting useful information from text. Related terms include text data mining (TDM) and text analytics. It is "the discovery by computer of new, previously unknown information by automatically extracting information from various written resources," according to one definition of the term. Websites, books, emails, reviews, and articles are all examples of written materials that may be utilized. Typically, the best way to acquire high-quality information is to construct patterns and trends through the use of methods such as statistical pattern learning. According to Hotho et al. (2005), we are able to differentiate between three distinct perspectives of text mining. These perspectives are information extraction, data mining, and a process known as knowledge discovery in databases (KDD). Text mining often entails the process of structuring the text that is input, determining patterns within the data that has been structured, and then lastly evaluating and interpreting the result of the mining process. When discussing text mining, the term "high quality" typically relates to some combination of the concepts of relevance, novelty, and interest. Text categorization, text clustering, concept/entity extraction, generation of granular taxonomies, sentiment analysis, document summarizing, and entity relation modeling are all examples of typical text mining activities. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Text Mining Chapter 2: Natural Language Processing Chapter 3: Data Mining Chapter 4: Information Extraction Chapter 5: Semantic Similarity Chapter 6: Unstructured Data Chapter 7: Biomedical Text Mining Chapter 8: Sentiment Analysis Chapter 9: Word Embedding Chapter 10: Social Media Mining (II) Answering the public top questions about text mining. (III) Real world examples for the usage of text mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of text mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of text mining.
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Book Synopsis Theory and Applications for Advanced Text Mining by : Shigeaki Sakurai
Download or read book Theory and Applications for Advanced Text Mining written by Shigeaki Sakurai and published by . This book was released on 2012 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Book Synopsis Theory and Applications for Advanced Text Mining by : Shigeaki Sakurai
Download or read book Theory and Applications for Advanced Text Mining written by Shigeaki Sakurai and published by BoD – Books on Demand. This book was released on 2012-11-21 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.
Book Synopsis Text Mining by : Michael W. Berry
Download or read book Text Mining written by Michael W. Berry and published by John Wiley & Sons. This book was released on 2010-05-03 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.
Book Synopsis The Text Mining Handbook by : Ronen Feldman
Download or read book The Text Mining Handbook written by Ronen Feldman and published by Cambridge University Press. This book was released on 2007 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description
Websites are a central part of today’s business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.
Book Synopsis Web Data Mining and the Development of Knowledge-Based Decision Support Systems by : Sreedhar, G.
Download or read book Web Data Mining and the Development of Knowledge-Based Decision Support Systems written by Sreedhar, G. and published by IGI Global. This book was released on 2016-12-21 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Websites are a central part of today’s business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.
"The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--
Book Synopsis Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications by : Gary Miner
Download or read book Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications written by Gary Miner and published by Academic Press. This book was released on 2012-01-11 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--
Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
Book Synopsis Mining Text Data by : Charu C. Aggarwal
Download or read book Mining Text Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2012-02-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
