"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.
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