The document discusses text mining and is divided into several sections. It begins with an introduction to text mining, explaining that it is the process of deriving useful information from unstructured text sources. It then covers text mining techniques like text preprocessing, transformation, attribute selection and data mining. Specific techniques discussed include document clustering, text representation as bags of words, and dimensionality reduction. The document also discusses how text mining can be applied to analyze documents in Google Sheets and extract key entities, concepts, and sentiment. It concludes that text mining generally involves analyzing text documents to extract important elements and then applying numeric data mining techniques to the processed text.

1. Text Mining
Maurice Masih
13030141093
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2. Topic of Discussion
• Introduction
• Text mining Comparison with other mining
• Text Mining Process
• How Algorithm is derived for Text Mining
• Text Analysis For Google Sheet
• Conclusion
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3. Introduction
• It is the process of deriving high-quality information
– Non trivial information
– Unstructured text.
• It is also called as text data mining or text analytics.
Need
Bio Tech Industry
80% of biological knowledge is only in research
paper(unstructured).
If a scientist manually read 50 research paper/week and only 10%
of data are useful then he/she manages only 5 research paper/week
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4. Text mining Comparison with…
Text Mining
Information
Retrieval
Web Mining
Data Mining
Statistics
Computer
Linguistics &
natural
language
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5. Text Mining Process
Text
transformation
Text
Preprocessing
Text
Attribute
Selection
Data Mining/
Patter Discovery
Interpretation/
Evaluation•Document
Clustering
•Text
Characteristics
•Text Cleanup
•Tokenization
•Text representation
•Feature Selection
•Reduce Dimensionality
•Remove irrelevant
attributes
•Structured
database
•Application
dependent
•Classic data mining
technique
Terminate or
iterate
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6. 1.Text
Document clustering
 Large volume of textual data.
 No clear picture what document suit the application.
 Common technique is K mean clustering.
Text Characteristics
 Dependency
 Ambiguity
 Noisy Data
 Unstructured data
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7. 2.Text Preprocessing
Text Cleanup
 Remove ads from page
 Convert from binary format
 Normalize text
 Deal with tables, figures and formulas
Tokenization
 Splitting up a string of characters into a set of tokens.
 Need to deal with issues like, Apostrophes, hyphens.
 Need to deal with tenses, part of speech, etc.
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8. 3.Text transformation
Text Representation
 Text document is represented by the words (features) it contains
and their occurrences.
Bag of Words
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9. 3.Text transformation contd..
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10. 4.Attribute Selection
Reduction of dimensionality
 Learners have difficulty addressing tasks with high dimensionality.
 Scarcity of resources and feasibility issues also call for a further
cutback of attributes.
Irrelevant features
 Not all features help!
e.g., the existence of a noun in a news article is unlikely to help
classify it as “politics” or “sport”.
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11. 5.Data Mining/ Pattern Discovery
 Text mining process merges with the traditional Data Mining process.
 Classic Data Mining techniques are used on the structured database
that resulted from the previous stages.
6.Interpretation & Evaluation
What to do next?
 Terminate
 Iterate
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12. How Algorithm is derived for Text
Mining
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13. Text Analysis For Google Sheet
•Perform Sentiment Analysis
•Extract mention of entities and
concepts.
•Summarize long chunks of text
•Detect the language of a
document
•Find the best hashtags .
•Extract the full text of an article,
as well as its author
name, embedded media, etc.
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14. Conclusion
Text mining generally consists of the analysis of (multiple) text
documents by extracting key phrases, concepts, matches etc. and
the preparation of the text processed in that manner for further
analyses with numeric data mining techniques.
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15. References
• http://www.r-bloggers.com/text-mining-in-r-automatic-categorization-
of-wikipedia-articles/
• http://www.kdd.org/sites/default/files/issues/7-1-2005-06/9-
Popowich.pdf
• www.Slideshare.net
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