MACHINE LEARNIGN

1. TEXT MINING
NAME:- AKASH KUMAR SINHA
CLASS:- CSE-A
College Roll:- 192015028 [15]
Univ. Roll:- 12500119160

2. Contents
• Introduction
• Data Mining Vs. Text Mining
• I/O model for Text Mining
• Steps for Text Mining
• Application of Text Mining
• Demerits of Text Mining
• Refrences

3. Introduction
• Text mining is a Discovery
• It is also known as Text Data
Mining(TDM)
• It is used to extract relevant
information or knowledge from
different sources which are
unstructured or semi-structured.

4. Data Mining vs Text Mining
Data Mining Text Mining
Process to extract information from
datasets
It’s a part of Data Mining which
processes the text only.
Databases are used to gather data Text is used to gather data.
Data is Homogeneous Data is Heterogeneous
Data is easy to Retrieve Data is not easy to Retrieve
Data is stored in Structured Format Data is stored in Un-structed Format

5. I/O MODEL FOR
TEXT MINING
Text
Mining
Technique
Pattern
Connections
Trends
OUTPUT
INPUT
TEXT
DOCUMENT

6. Steps for Text
Mining
Pre-Processing the Text
Applying Text Mining Techniques
 Information Extraction
 Information Retrieval
 Categorization
 Clustering
 Summarization
Analysing the Text

7. Application of Text
Mining
Analysis of Market trends
 Classification Technique
 Information Extraction Technique
Analysis and Screening of Junk Emails
 Classification based on pre-defined frequently
occurring items.

8. Merits of Text
Mining
 Extraction of relevant information and
relationships from natural documents
 Extraction of Information from Unstructured or
Semi Structured Documents
 Database limits itself to storage of less
information whereas Text Mining overcomes this
Limitation.

9. Demerits of Text
Mining
× Requires Initial Learned Information System
for Initial Extraction
× Suitable programs are not been defined to
Analyze Text from Mining Knowledge or
Information
× High potential to gather garbled or false
results

10. THANK YOU