Ebook: Mining Text Data
- Tags: Database Management, Data Mining and Knowledge Discovery, Information Systems Applications (incl. Internet), Computer Communication Networks, Multimedia Information Systems
- Year: 2012
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xi
An Introduction to Text Mining....Pages 1-10
Information Extraction from Text....Pages 11-41
A Survey of Text Summarization Techniques....Pages 43-76
A Survey of Text Clustering Algorithms....Pages 77-128
Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond....Pages 129-161
A Survey of Text Classification Algorithms....Pages 163-222
Transfer Learning for Text Mining....Pages 223-257
Probabilistic Models for Text Mining....Pages 259-295
Mining Text Streams....Pages 297-321
Translingual Mining from Text Data....Pages 323-359
Text Mining in Multimedia....Pages 361-384
Text Analytics in Social Media....Pages 385-414
A Survey of Opinion Mining and Sentiment Analysis....Pages 415-463
Biomedical Text Mining: A Survey of Recent Progress....Pages 465-517
Back Matter....Pages 519-522
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.
Content:
Front Matter....Pages i-xi
An Introduction to Text Mining....Pages 1-10
Information Extraction from Text....Pages 11-41
A Survey of Text Summarization Techniques....Pages 43-76
A Survey of Text Clustering Algorithms....Pages 77-128
Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond....Pages 129-161
A Survey of Text Classification Algorithms....Pages 163-222
Transfer Learning for Text Mining....Pages 223-257
Probabilistic Models for Text Mining....Pages 259-295
Mining Text Streams....Pages 297-321
Translingual Mining from Text Data....Pages 323-359
Text Mining in Multimedia....Pages 361-384
Text Analytics in Social Media....Pages 385-414
A Survey of Opinion Mining and Sentiment Analysis....Pages 415-463
Biomedical Text Mining: A Survey of Recent Progress....Pages 465-517
Back Matter....Pages 519-522
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