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Abstract

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

Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.

LCSH

Data mining

Subject

Data mining

Theme

Data Mining

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Content

Enthält die Beiträge: CROFT, W.B.: Combining approaches to information retrieval; GREIFF, W.R.: The use of exploratory data analysis in information retrieval research; PONTE, J.M.: Language models for relevance feedback; PAPKA, R. u. J. ALLAN: Topic detection and tracking: event clustering as a basis for first story detection; CALLAN, J.: Distributed information retrieval; XU, J. u. W.B. CROFT: Topic-based language models for ditributed retrieval; LU, Z. u. K.S. McKINLEY: The effect of collection organization and query locality on information retrieval system performance; BALLESTEROS, L.A.: Cross-language retrieval via transitive translation; SANDERSON, M. u. D. LAWRIE: Building, testing, and applying concept hierarchies; RAVELA, S. u. C. LUO: Appearance-based global similarity retrieval of images