Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
The Text Mining Handbook
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Editorial Reviews
Review
"...buy the book. This book is definitely worth having in your book shelf as a handy reference."
L. Venkata Subramaniam IAPR Newsletter
"A good introduction to text mining written by leading experts in the field. The book is well written and addresses both the theory and practice of text mining, which makes it appealing for researchers and practitioners alike... Highly recommended to those who would like to start delving into the area of text mining without having any previous background in computational linguistics."
Rada Mihalcea, University of North Texas, for Computational Linguistics
L. Venkata Subramaniam IAPR Newsletter
"A good introduction to text mining written by leading experts in the field. The book is well written and addresses both the theory and practice of text mining, which makes it appealing for researchers and practitioners alike... Highly recommended to those who would like to start delving into the area of text mining without having any previous background in computational linguistics."
Rada Mihalcea, University of North Texas, for Computational Linguistics
Book Description
Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world applications.
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Most Helpful Customer Reviews
17 of 18 people found the following review helpful:
5.0 out of 5 stars
Great overview,
Amazon Verified Purchase(What's this?)
This review is from: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Hardcover)
This was one of the few books that included a very clear and extensive treatment of information extraction techniques. There were plenty of diagrams which is great for a visual learner. All the techniques are explained using both plain English and formulas, so that you can pick up the scientific notation with minimal previous knowledge.
Even when the authors plug their own company and research at the end it was moderately useful in illustrating the concepts mentioned in a real world scenario.
4 of 4 people found the following review helpful:
3.0 out of 5 stars
Ok overview but not sufficient,
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Amazon Verified Purchase(What's this?)
This review is from: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Hardcover)
This book is a decent overview of the field but I don't think it will prepare anyone to actually create text mining systems. Feels like its oriented to technical managers rather than the people who will be actually building the stuff. Examples are really lagging, the very little psuedo code that does exists is not that good. It has very good definitions of concepts and explains what types of algorithms you'll be using but its very much like an encyclopedia. After reading through this book i quickly purchased Konchandy's Text Mining book to see if it was better. I haven't finished it yet but so far its great, would recommend that book over this one if you actually have to build text mining systems. On the up side, this book has very comprehensive references to find more information on each section which could prove very useful.
2 of 2 people found the following review helpful:
3.0 out of 5 stars
New material but not really applicable,
By Mark Alen "GradStudent82" (CA, USA) - See all my reviews
This review is from: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Hardcover)
I was very disappointed by this book. It has some good material but examples are really missing. It might be a good book for technical managers to have a high level picture of the field. As a graduate student I don't seem to be able to use this book in any practical project.
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Inside This Book
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First Sentence:
Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
information extraction, visualization approaches, link analysis, anaphora resolution, interaction database, query interpreter, machine learning approach, frequent concept sets, mining preprocessing operations, many text mining systems, core mining operations, simple concept graphs, concept association graph, text mining operations, pattern overabundance, knowledge discovery operations, generalized background knowledge, maximal association rule, promoted border, text mining applications, spring graph, distribution browser, fisheye approaches, patent manager, background knowledge sources Key Phrases - Capitalized Phrases (CAPs): (learn more)
Industry Analyzer, Patent Researcher, Document Explorer, Core Text Mining Operations, Nawaq Alhamzi, Ahmed Alhaznawi, Abdulaziz Alomari, Salem Alhamzi, Recall Prec, South American, Ziad Jarrahi, Ahmed Alnami, Mohald Alshehri, Wail Alshehri, Fayez Ahmed, Mohamed Atta, Ahmed Alghamdi, Hamza Alghamdi, Saeed Alghamdi, Title Browser, Hani Hanjour, Satam Al Suqami, West Germany, Khalid Al-Midhar, Real-World Example Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
information extraction, visualization approaches, link analysis, anaphora resolution, interaction database, query interpreter, machine learning approach, frequent concept sets, mining preprocessing operations, many text mining systems, core mining operations, simple concept graphs, concept association graph, text mining operations, pattern overabundance, knowledge discovery operations, generalized background knowledge, maximal association rule, promoted border, text mining applications, spring graph, distribution browser, fisheye approaches, patent manager, background knowledge sources Key Phrases - Capitalized Phrases (CAPs): (learn more)
Industry Analyzer, Patent Researcher, Document Explorer, Core Text Mining Operations, Nawaq Alhamzi, Ahmed Alhaznawi, Abdulaziz Alomari, Salem Alhamzi, Recall Prec, South American, Ziad Jarrahi, Ahmed Alnami, Mohald Alshehri, Wail Alshehri, Fayez Ahmed, Mohamed Atta, Ahmed Alghamdi, Hamza Alghamdi, Saeed Alghamdi, Title Browser, Hani Hanjour, Satam Al Suqami, West Germany, Khalid Al-Midhar, Real-World Example Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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