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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
Add image field to pdf form - C# PDF Field Edit Library: insert, delete, update pdf form field in C#.net, ASP.NET, MVC, Ajax, WPF
Online C# Tutorial to Insert, Delete and Update Fields in PDF Document
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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
C# PDF insert image Library: insert images into PDF in C#.net, ASP
Insert images into PDF form field. Access to freeware download and online C#.NET class source code. How to insert and add image, picture, digital photo, scanned
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VB.NET PDF insert image library: insert images into PDF in vb.net
Add images to any selected PDF page in VB.NET. attributes adjust functionalities, such as resize image by zooming Insert images into PDF form field in VB.NET.
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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
VB.NET PDF Form Data Read library: extract form data from PDF in
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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
VB.NET PDF Form Data fill-in library: auto fill-in PDF form data
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Following C# sample code can help you have a quick evaluation of it. C#.NET Demo Code: Auto Fill-in Field Data to PDF in C#.NET. Add necessary references:
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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#
Scan image to PDF, tiff and various image formats. Get image information, such as its location, zonal information Able to edit, add, delete, move, and output PDF
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VB.NET PDF Image Extract Library: Select, copy, paste PDF images
multiple types of image from PDF file in VB.NET, like XObject Image, XObject Form, Inline Image DLLs for PDF Image Extraction in VB.NET. Add necessary references
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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
8
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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Wednesday, July 10, 13
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