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.

About the Author

Michael W. Berry, Professor and Associate Department Head, Department of Electrical Engineering and Computer Science, University of Tennessee.
Michael is on the Editorial board of Computing in Science and Engineering and Statistical Analysis and Data Mining Journals.

Jacob Kogan, Department of Mathematics and Statistics, University of Maryland Baltimore County, USA.

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