This document discusses text mining. It begins with an introduction that defines text mining as extracting relevant information, knowledge, or patterns from unstructured or semi-structured sources. It then compares text mining to data mining, noting that text mining involves linguistic processing of semi-structured and unstructured data like text, while data mining analyzes structured numeric data. The document outlines the motivation, steps, key terms, merits, applications, and demerits of text mining.

1. TEXT MINING
BY- ADITYA SHARMA
BCA-3E
03921402021

2. CONTENTS
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1) INTRODUCTION
2) DATA MINING Vs. TEXT MINING
3) MOTIVATION FOR TEXT MINING
4) STEPS FOR TEXT MINING
5) KEY TERMS IN TEXT MINING
6) MERITS OF TEXT MINING
7) APPLICATIONS OF TEXT MINING
8) DEMERITS OF TEXT MINING
9) REFERENCES

3. INTRODUCTION
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1) Text Mining is a Discovery
2) Text Mining is also referred as Text Data Mining (TDM) and Knowledge Discovery in Textual Database (KDT). Text
Mining is used to extract relevant information or knowledge or pattern from different sources that are in unstructured or
semi-structured form.
3) Text Mining is used to extract relevant information or knowledge or pattern from different sources that are in
unstructured or semi-structured form.
4) Extract and discover knowledge hidden in text automatically
5) Aid domain experts by automatically:
identifying concepts
extracting facts/relations
discovering implicit links
generating hypotheses

4. DATA MINING VS. TEXT MINING
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5. 5
Data Mining Text Mining
Process directly Linguistic processing or natural
language processing (NLP)
Identify causal relationship Discover heretofore unknown
information
Structured Data Semi-structured & Unstructured
Data (Text)
Structured numeric transaction data
residing in rational data warehouse
Applications deal with much more
diverse and eclectic collections of
systems and formats

6. MOTIVATION FOR TEXT MINING
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7. 7
1) Approximately 90% of the world’s data is held in unstructured formats (source:
Oracle Corporation)
2) Information intensive business processes demand that we transcend from simple
document retrieval to “knowledge” discovery.

8. STEPS FOR TEXT MINING
1) Pre-Processing the Text
2) Applying Text Mining Techniques
-Summarization
-Classification
-Clustering
-Visualization
-Information Extraction
3) Analyzing the Text
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9. KEY TERMS IN TEXT MINING
1) Information Extraction (IE)
-The science of searching for
-Information in documents
-Documents themselves
-Metadata which describe documents
-Text, sound, images or data, within database: relational stand-alone database or hypertext
networked databases such as the Internet or intranets.
2) Artificial Intelligence (AI)
-Artificial intelligence (AI) is a branch of computer science and engineering that deals with
intelligent behavior, learning, and adaptation in machines
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10. Merits of text mining
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Database limits itself to Storage of less Information whereas Text Mining
overcomes this limitation
Extraction of relevant Information and Relationships from Natural Documents
Extraction of Information from Unstructured or Semi- structured Documents

11. Applications of text mining
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12. 12
Analysis of Market Trends
-Classification Technique
-Information Extraction Technique
Analysis and Screening of Junk Emails
-Classification on the basis of pre-defined frequently occurring items

13. Demerits of text mining
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1. Requires Initial Learned Information System for Initial Extraction
2. Suitable programs are not been defined to Analyze Text from Mining Knowledge or Information
3. Misguided interpretations or the misuse of information.

14. references
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1) www.wikipdedia.com
2) Geeksforgeeks.org
3) www.howtogeek.com
4) www.techopedia.com

15. THANK YOU