Berry M., Kogan J. Text Mining Applications and Theory

John Wiley, 2010. — 405 p. — ISBN-10 0470749822; ISBN-13 978-0470749821Text 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.Text Extraction, Classification, and Clustering
Automatic keyword extraction from individual documents
Algebraic techniques for multilingual document clustering
Content-based spam email classification using machine-learning algorithms
Utilizing nonnegative matrix factorization for email classification problems
Constrained clustering with k-means type algorithms
Anomaly and Trend Detection
Survey of text visualization techniques
Adaptive threshold setting for novelty mining
Text mining and cybercrime
Text Streams
Events and trends in text streams
Embedding semantics in LDA topic models