Text Mining Applications and Theory

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.

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.

List of Contributors	p. xi
Preface	p. xiii
Text Extraction, Classification, And Clustering	p. 1
Automatic keyword extraction from individual documents	p. 3
Introduction
Keyword extraction methods	p. 4
Rapid automatic keyword extraction	p. 5
Candidate keywords	p. 6
Keyword scores	p. 7
Adjoining keywords	p. 8
Extracted keywords	p. 8
Benchmark evaluation	p. 9
Evaluating precision and recall	p. 9
Evaluating efficiency	p. 10
Stoplist generation	p. 11
Evaluation on news articles	p. 15
The MPQA Corpus	p. 15
Extracting keywords from news articles	p. 15
Summary	p. 18
Acknowledgements	p. 19
References	p. 19
Algebraic techniques for multilingual document clustering	p. 21
Introduction	p. 21
Background	p. 22
Experimental setup	p. 23
Multilingual LSA	p. 25
Tucker l method	p. 27
PARAFAC2 method	p. 28
LSA with term alignments	p. 29
Latent morpho-semantic analysis (LMSA)	p. 32
LMSA with term alignments	p. 33
Discussion of results and techniques	p. 33
Acknowledgements	p. 35
References	p. 35
Content-based spam email classification using machine-learning algorithms	p. 37
Introduction	p. 37
Machine-learning algorithms	p. 39
Naive Bayes	p. 39
LogitBoost	p. 40
Support vector machines	p. 41
Augmented latent semantic indexing spaces	p. 43
Radial basis function networks	p. 44
Data preprocessing	p. 45
Feature selection	p. 45
Message representation	p. 47
Evaluation of email classification	p. 48
Experiments	p. 49
Experiments with PU1	p. 49
Experiments with ZH1	p. 51
Characteristics of classifiers	p. 53
Concluding remarks	p. 54
Acknowledgements	p. 55
References	p. 55
Utilizing nonnegative matrix factorization for email classification problems	p. 57
Introduction	p. 57
Related work	p. 59
Synopsis	p. 60
Background	p. 60
Nonnegative matrix factorization	p. 60
Algorithms for computing NMF	p. 61
Datasets	p. 63
Interpretation	p. 64
NMF initialization based on feature ranking	p. 65
Feature subset selection	p. 66
FS initialization	p. 66
NMF-based classification methods	p. 70
Classification using basis features	p. 70
Generalizing LSI based on NMF	p. 72
Conclusions	p. 78
Acknowledgements	p. 79
References	p. 79
Constrained clustering with k-means type algorithms	p. 81
Introduction	p. 81
Notations and classical k-means	p. 82
Constrained k-means with Bregman divergences	p. 84
Quadratic k-means with cannot-link constraints	p. 84
Elimination of must-link constraints	p. 87
Clustering with Bregman divergences	p. 89
Constrained smoka type clustering	p. 92
Constrained spherical k-means	p. 95
Spherical k-means with cannot-link constraints only	p. 96
Spherical k-means with cannot-link and must-link constraints	p. 98
Numerical experiments	p. 99
Quadratic k-means	p. 100
Spherical k-means	p. 100
Conclusion	p. 101
References	p. 102
Anomaly and Trend Detection	p. 105
Survey of text visualization techniques	p. 107
Visualization in text analysis	p. 107
Tag clouds	p. 108
Authorship and change tracking	p. 110
Data exploration and the search for novel patterns	p. 111
Sentiment tracking	p. 111
Visual analytics and FutureLens	p. 113
Scenario discovery	p. 114
Scenarios	p. 115
Evaluating solutions	p. 115
Earlier prototype	p. 116
Features of FutureLens	p. 117
Scenario discovery example: bioterrorism	p. 119
Scenario discovery example: drug trafficking	p. 121
Future work	p. 123
References	p. 126
Adaptive threshold setting for novelty mining	p. 129
Introduction	p. 129
Adaptive threshold setting in novelty mining	p. 131
Background	p. 131
Motivation	p. 132
Gaussian-based adaptive threshold setting	p. 132
Implementation issues	p. 137
Experimental study	p. 138
Datasets	p. 138
Working example	p. 139
Experiments and results	p. 142
Conclusion	p. 146
References	p. 147
Text mining and cybercrime	p. 149
Introduction	p. 149
Current research in Internet predation and cyberbullying	p. 151
Capturing IM and IRC chat	p. 151
Current collections for use in analysis	p. 152
Analysis of IM and IRC chat	p. 153
Internet predation detection	p. 153
Cyberbullying detection	p. 158
Legal issues	p. 159
Commercial software for monitoring chat	p. 159
Conclusions and future directions	p. 161
Acknowledgements	p. 162
References	p. 162
Text Streams	p. 165
Events and trends in text streams	p. 167
Introduction	p. 167
Text streams	p. 169
Feature extraction and data reduction	p. 170
Event detection	p. 171
Trend detection	p. 174
Event and trend descriptions	p. 176
Discussion	p. 180
Summary	p. 181
Acknowledgements	p. 181
References	p. 181
Embedding semantics in LDA topic models	p. 183
Introduction	p. 183
Background	p. 184
Vector space modeling	p. 184
Latent semantic analysis	p. 185
Probabilistic latent semantic analysis	p. 185
Latent Dirichlet allocation	p. 186
Graphical model and generative process	p. 187
Posterior inference	p. 187
Online latent Dirichlet allocation (OLDA)	p. 189
Illustrative example	p. 191
Embedding external semantics from Wikipedia	p. 193
Related Wikipedia articles	p. 194
Wikipedia-inffuenced topic model	p. 194
Data-driven semantic embedding	p. 194
Generative process with data-driven semantic embedding	p. 195
OLDA algorithm with data-driven semantic embedding	p. 196
Experimental design	p. 197
Experimental results	p. 199
Related work	p. 202
Conclusion and future work	p. 202
References	p. 203
Index	p. 205
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