Text mining : applications and theory
Author(s)
Bibliographic Information
Text mining : applications and theory
John Wiley & Sons, 2010
Available at 21 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"