This document summarizes a webinar on text mining presented by Dr. A. Muthusamy. The webinar covered an introduction to data types and text mining, the need for text mining in analyzing large amounts of unstructured text data, the objectives and process of text mining, and applications and benefits of text mining such as knowledge discovery, predictive modeling, and information extraction. Challenges of text mining include overcoming database storage limits and handling natural language processing ambiguities.

1. WEBINAR ON
Presented by
Dr. A. MUTHUSAMY
Head, Associate Professor
Department of Computer Science with Cognitive Systems
Dr. N.G.P. Arts and Science College
Coimbatore-641 048
Tamil Nadu, India
Mobile: +91 8675390072, E-mail: muthusamy@drngpasc.ac.in 1
TM

2. OUTLINE
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Introduction - Data & Its Types
Need of Text Mining
Objectives of Text Mining
Search Vs Discover
Relevant Disciplines
Process & Tools - Text Mining
Benefits & Applications of TM
Conclusion

3. INTRODUCTION

4. ABOUT DATA
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• Data - collection of raw facts & symbols
• Data is collected from multiple sources
• Data can be of the following forms
 Text – Alphabets
 Number – 0 to 9
 Image – Scanner
 Audio – Microphone
 Video – Camera
 Sensors

5. ABOUT DATA
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6. ABOUT DATA
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• Every organization has its own specific data - to perform certain
operations within it
• It gives the status of past activities and enable us to make decision
• Information Processing – Converting data into information

7. REAL TIME SCENARIOS
• Many individuals use some form of computing every day
whether they realize it or not
• Swiping a debit card, sending an email / using a mobile phone
can all be considered forms of computing
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8. DATA VARIETIES
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• Data that has no inherent structure & is
usually stored as different types of files
• Examples: Text docs, PDFs, images & Videos
• Textual data with erratic formats that can be
formatted with effort & S/W tools
• Examples: Click stream data
• Textual data files with an apparent pattern,
enabling analysis
• Examples: Spreadsheets & XML files
• Data having a defined data model, format,
structure
• Example: Database

9. DATA VARIETIES
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Structured Unstructured

10. 10
TM
TM

11. NEED OF TEXT MINING
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• Approximately 90% of the world’s data is held in unstructured formats
(source: Oracle Corporation)
• Most of the information in public and private sectors are stored
electronically in the form of text databases
• Information intensive business processes demand that we transcend
from simple document retrieval to Knowledge Discovery
Text Databases

12. NEED OF TEXT MINING
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Why Cats sit on mats?

13. NEED OF TEXT MINING
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• It would be impossible to read all the millions of research articles on
the topic yourself.
• Text mining helps to filter large amounts of research and extracts the
relevant information
• It helps to identify, ‘cat’ is the noun, ‘sit’ is the verb and ‘on’ is the
proposition

14. NEED OF TEXT MINING
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• It is not just a search tool, it can also understand that the ‘cat’ is an
animal, ‘sit’ is an action, and a ‘mat’ is an object.
• Identifies and maps patterns and trends across the millions of articles.
Answer: Most of the cats who sit on mats come from cold climates.

16. TEXT MINING
• Knowledge Discovery of Text (KDT) - Large collections of written
resources to generate valuable information from unstructured formats
• Transform the unstructured text into structured data
• It involves algorithms of DM, ML, Statistics, and NLP, attempts to
extract high quality of useful information
• To exploit information contained in textual documents in various ways
including pattern discovery and trends in data, associations among
entities, predictive rules etc.,
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17. TEXT MINING
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Framework to Create Dictionary

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19. SEARCH Vs DISCOVER
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20. SEARCH Vs DISCOVER
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Find records within a structured database
• Database Type : Structured
• Search Mode : Goal-driven
• Atomic entity : Data Record
• Information Need : Find a Japanese restaurant in Bangalore that
serves vegetarian food
• Query : SELECT * FROM restaurants WHERE city =
Bangalore AND type = Japanese AND has_veg =
true
Data Retrieval

21. SEARCH Vs DISCOVER
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Find relevant information in an unstructured information source (usually text)
• Database Type : Unstructured
• Search Mode : Goal-driven
• Atomic entity : Document
• Information Need : Find a Japanese restaurant in Bangalore
• Query : “Japanese restaurant Bangalore” or
Bangalore ->Restaurants->Japanese
Inf. Retrieval

22. SEARCH Vs DISCOVER
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Discover new knowledge through analysis of data
• Database Type : Structured
• Search Mode : Opportunistic
• Atomic entity : Numbers and Dimensions
• Information Need : Show trend over time in # of visits to
Japanese restaurants in Bangalore
• Query : SELECT SUM(visits) FROM restaurants WHERE city
=Bangalore AND type=Japanese ORDER BY date
Data Mining

23. SEARCH Vs DISCOVER
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Discover new knowledge through analysis of text
• Database Type : Unstructured
• Search Mode : Opportunistic
• Atomic entity : Language feature or concept
• Information Need : Find the types of food poisoning most often
associated with Japanese restaurants
• Query : Rank diseases found associated with “Japanese
restaurants”
Text Mining

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25. RELEAVANT DISCIPLINES
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Information Extraction (IE)
• The process of automatically obtaining structured data from unstructured
data
Natural Language Processing (NLP)
• Study of human language - primary component of Artificial Intelligence (AI)
• Computational technique to analysis & synthesis of natural language &
speech

26. RELEAVANT DISCIPLINES
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Data Mining (DM)
• It refers to the extraction of useful data, hidden patterns from large data
sets.
• Data mining tools can predict behaviors & future trends that allow
businesses to make a better data-driven decision
Information Retrieval (IR)
• It is the process of obtaining information system resources that are
relevant to an information need from a collection of those resources
• IR an extension to document extraction
• It helps to narrow down the set of records that are associated with a
specific problem

27. RELEAVANT DISCIPLINES
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Example
Named Entity Recognition

28. P
R
O
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S
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T
M

29. PROCESS OF TEXT MINING
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30. PROCESS OF TEXT MINING
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• Document extract
from different
external sources
• Web pages
Extract Docs.
• Document storage
• Handle multiple
documents at a
time
Corpus
• Tokenization, PoS
Tagging
• Stemming,
Lemmatization &
Stop words
Text Transformation
• Bags of Words
• Vector space
Extract Feature
• TF - IDF
• SVD & LDA
Reduce Dimension
• Supervised
• Unsupervised
Data Mining

31. EXAMPLES
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Tokenization
• Tokenization is just the process of splitting a sentence into words
Tokenization of a sentence

32. EXAMPLES
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Stemming and Lemmatization
• Stemming and Lemmatization is the method to normalize the text documents
• Text normalization helps to improve the vocabulary (reducing the inflectional
forms) and accuracy of many language modeling tasks
• It is the process of converting a word to its base form

33. EXAMPLES
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PoS Tagging
• Processing text and assigning parts of speech to each word
• Tags may vary by different tagging sets as, Noun, Verb, Preposition,
Determiner etc.

34. EXAMPLES
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35. EXAMPLES
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36. EXAMPLES
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Reduce Dimensions
• Each row in the document / term matrix represents each document as a
high dimension vector (each dimension represents the occurrence of the
term)
• It can be measured by, tf-idf
 The reason, to reduce the time & memory
 To transform the vector from term space to a topic space - allows
document of similar topics to associate with each other use different
terms
• Example: "pet" and "cat" are map to the same topic based on their
co-occurrence

37. EXAMPLES
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Data Exploration
• By computing term frequency, Word cloud & Histogram are presented to
represent the significance of the terms
• To prove the correctness of the algorithm include statistical tests within the
model
Data Visualization

38. TOOLS
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39. TEXT ANALYTICS

40. BENEFITS OF TEXT MINING
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Extract all kinds of
information
E-commerce –
personalized marketing
Society - identifying
criminal activities
Companies - better
customer relationship
Scalability &
Real Time Analysis
Predictive Modeling

41. APPLICATIONS OF TEXT MINING
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Search engine technology
Analysis of survey data
Spam identification
Call center routing
Surveillance
Public health early warning

42. CONCLUSION
• Overcome the database storage limits
• Extraction of relevant information & relationships from
unstructured docs
• Empirically proven as more accurate & highly effective
• Domain knowledge integration
• Multilingual text refinement
• Varying concepts granularity
• NLP ambiguity
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CHALLENGES

43. Queries
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