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© Abbott Analytics, Inc. 2001-2013
Introduction to Text Mining
Virtual Data Intensive Summer School
July 10, 2013
Dean Abbott
Abbott Analytics, Inc.
email: dean@abbottanalytics.com
url: http://www.abbottanalytics.com
blog: http://abbottanalytics.blogspot.com
Twitter: @deanabb
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Wednesday, July 10, 13
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How to Insert, Delete and Update Fields in PDF Document with VB.NET Demo Code
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© Abbott Analytics, Inc. 2001-2013
Why Text?
How much data? 1.8 zettabytes (1.8 trillion GB)
Most of the World’s Data is Unstructured
2009 HP survey
: 70%
•Gartner: 80%
Jerry Hill (Teradata), Anant Jhingran (IBM): 85%
Structured (stored) data often misses elements critical to 
predictive modeling
•Un-transcribed fields, notes, comments
Ex: examiner/adjuster notes, surveys with free-text 
fields, medical charts
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Wednesday, July 10, 13
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© Abbott Analytics, Inc. 2001-2013
Why Text Mining?
Leveraging text should improve decisions and predictions
• Text mining is gaining momentum
Sentiment Analysis (twitter, facebook)
Predicting stock market
•Predicting churn
Customer influence
Customer Service and Help Desk
Not to mention Watson!
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Wednesday, July 10, 13
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© Abbott Analytics, Inc. 2001-2013
Structured vs. 
Unstructured Data
Structured data
“Loadable into a spreadsheet”
Rows and columns
Each cell filled, or could be filled
Data is consistent, uniform
Data mining friendly
Unstructured data
Microsoft Word, HTML, Adobe PDF documents, ...
This PPT document is unstructured text
Unstructured data often converted to XML -> semi-structured
Not structured into “cells”
Variable record length; notes, free-form survey answers
Text is relatively sparse, inconsistent, and not uniform
Also...images, video, music, etc.
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Wednesday, July 10, 13
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© Abbott Analytics, Inc. 2001-2013
How Unstructured is 
“Unstructured”?
• Feldman and Sanger
“Weakly Structured” data: few structural 
cues to text based on layout or markups
• Research papers
• Legal memoranda
News Stories
“Semistructured” data: extensive format 
elements, metadata, field labels
• Email
HTML web pages
• PDF files
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Wednesday, July 10, 13
C# PDF Image Extract Library: Select, copy, paste PDF images in C#
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© Abbott Analytics, Inc. 2001-2013
Why is Text Mining Hard
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• Language is ambiguous
• Context is needed to clarify
• The same words can mean different things (homographs)
• Bear (verb) - to support or carry
• Bear (noun) - a large animal
• Different words can mean the same thing (synonyms)
• Language is subtle
• Concept / Word extraction usually results in huge 
number of “dimensions”
• Thousands of new fields
• Each field typically has low information content (sparse)
• Mispellings, abbreviations, spelling variants
• Renders search engines, SQL queries, Regex, ... ineffective
Wednesday, July 10, 13
© Abbott Analytics, Inc. 2001-2013
Four Text Mining Ambiguities
Homonomy: same word, different 
meaning by accident of history
Bank
• a. Mary walked along the bank
of the 
river.
b. HarborBank is the richest bank
in the 
city.
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Polysemy: same word or form, but 
different, albeit related meaning
Bank
a. The bank
raised its interest rates yesterday.
b. The store is next to the newly constructed 
bank
.
c. The bank
appeared first in Italy in the 
Renaissance.
Synonymy: synonyms, different words, 
similar or same meaning; can 
substitute one word for the other 
without changing the meaning of the 
sentence substantively.
Synonyms can have differing connotations...
a. Miss Nelson became a kind of big
sister to 
Benjamin.
b. Miss Nelson became a kind of large
sister to 
Benjamin.
Hyponymy: concept hierarchy or 
subclass (subordinates)
Animal (noun)
a. dog
b. cat
Injury
a. Broken leg, contusion...
Wednesday, July 10, 13
© Abbott Analytics, Inc. 2001-2013
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Databases
Library and
Information Sciences
Text Mining
Statistics
AI and Machine Learning
Data Mining
Computational Linguistics
* Natural 
Language 
Processing
* Information 
Retrieval
* Text
Classification
* Text
Clustering
* Information 
Extraction
* Web 
Mining
* Concept
Extraction
From Practical Text Mining 
(Delen, Fast, Hill, Miner, Elder, Nisbet)
Wednesday, July 10, 13
© Abbott Analytics, Inc. 2001-2013
Seven Types of  Text Mining
(from Miner, Elder, et al)
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Wednesday, July 10, 13
© Abbott Analytics, Inc. 2001-2013
Seven Types of  Text Mining
(from Miner, Elder, et al)
1. Search and Information Retrieval (IR):  Storage and retrieval of text documents, including search 
engines and keyword search
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