Text Mining

Applications and Theory
Herausgeber: Berry, Michael W; Kogan, Jacob

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Seitenzahl: 224
• Erscheinungstermin: 3. Mai 2010
• Englisch
• Abmessung: 239mm x 159mm x 17mm
• Gewicht: 488g
• ISBN-13: 9780470749821
• ISBN-10: 0470749822
• Artikelnr.: 28707756

Autorenporträt

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.

Inhaltsangabe

List of Contributors. Preface. PART I TEXT EXTRACTION, CLASSIFICATION, AND
CLUSTERING. 1 Automatic keyword extraction from individual documents. 1.1
Introduction. 1.2 Rapid automatic keyword extraction. 1.3 Benchmark
evaluation. 1.4 Stoplist generation. 1.5 Evaluation on news articles. 1.6
Summary. 1.7 Acknowledgements. 2 Algebraic techniques for multilingual
document clustering. 2.1 Introduction. 2.2 Background. 2.3 Experimental
setup. 2.4 Multilingual LSA. 2.5 Tucker1 method. 2.6 PARAFAC2 method. 2.7
LSA with term alignments. 2.8 Latent morpho-semantic analysis (LMSA). 2.9
LMSA with term alignments. 2.10 Discussion of results and techniques. 2.11
Acknowledgements. 3 Content-based spam email classification using
machine-learning algorithms. 3.1 Introduction. 3.2 Machine-learning
algorithms. 3.3 Data preprocessing. 3.4 Evaluation of email classification.
3.5 Experiments. 3.6 Characteristics of classifiers. 3.7 Concluding
remarks. 3.8 Acknowledgements. 4 Utilizing nonnegative matrix factorization
for email classification problems. 4.1 Introduction. 4.2 Background. 4.3
NMF initialization based on feature ranking. 4.4 NMF-based classification
methods. 4.5 Conclusions. 4.6 Acknowledgements. 5 Constrained clustering
with k-means type algorithms. 5.1 Introduction. 5.2 Notations and classical
k-means. 5.3 Constrained k-means with Bregman divergences. 5.4 Constrained
smoka type clustering. 5.5 Constrained spherical k-means. 5.6 Numerical
experiments. 5.7 Conclusion. PART II ANOMALY AND TREND DETECTION. 6 Survey
of text visualization techniques. 6.1 Visualization in text analysis. 6.2
Tag clouds. 6.3 Authorship and change tracking. 6.4 Data exploration and
the search for novel patterns. 6.5 Sentiment tracking. 6.6 Visual analytics
and FutureLens. 6.7 Scenario discovery. 6.8 Earlier prototype. 6.9 Features
of FutureLens. 6.10 Scenario discovery example: bioterrorism. 6.11 Scenario
discovery example: drug trafficking. 6.12 Future work. 7 Adaptive threshold
setting for novelty mining. 7.1 Introduction. 7.2 Adaptive threshold
setting in novelty mining. 7.3 Experimental study. 7.4 Conclusion. 8 Text
mining and cybercrime. 8.1 Introduction. 8.2 Current research in Internet
predation and cyberbullying. 8.3 Commercial software for monitoring chat.
8.4 Conclusions and future directions. 8.5 Acknowledgements. PART III TEXT
STREAMS. 9 Events and trends in text streams. 9.1 Introduction. 9.2 Text
streams. 9.3 Feature extraction and data reduction. 9.4 Event detection.
9.5 Trend detection. 9.6 Event and trend descriptions. 9.7 Discussion. 9.8
Summary. 9.9 Acknowledgements. 10 Embedding semantics in LDA topic models.
10.1 Introduction. 10.2 Background. 10.3 Latent Dirichlet allocation. 10.4
Embedding external semantics from Wikipedia. 10.5 Data-driven semantic
embedding. 10.6 Related work. 10.7 Conclusion and future work. References.
Index.