This document discusses text mining, including what it is, common techniques used, and applications. It begins by defining text mining as the process of transforming unstructured text into a structured format to identify meaningful patterns and insights. It then discusses key text mining techniques such as natural language processing, information extraction, and data mining. Finally, it outlines several common applications of text mining including information retrieval, customer service, risk management, maintenance, healthcare, and spam filtering.

1. Text Mining

2. Gr-I
● Akash Kundu(11500220042)
● Bajrang(11500220045)
● Suchit Kumar(11500220046)
● Aditya Keshari(11500220047)
● Pranav Praveen(11500220048)

3. Text Mining
● What is text mining?
● Text mining techniques
● Text mining applications

4. Text

5. Text
Text is one of the most common data types within databases. Depending on the
database, this data can be organized as:
● Structured data
● Unstructured data
● Semi-structured data

6. Mining

7. What is text mining?

8. What is Text mining
❖ Text mining, also known as text data mining, is the process of transforming
unstructured text into a structured format to identify meaningful patterns and new
insights.
❖ By applying advanced analytical techniques, such as Naïve Bayes, Support Vector
Machines (SVM), and other deep learning algorithms, companies are able to
explore and discover hidden relationships within their unstructured data.

9. Text mining techniques

10. Text mining
applications
● Information retrieval
● Natural language
processing (NLP)
● Information extraction
● Data mining

11. Text mining applications

12. Text mining techniques
The process of text mining comprises several activities that enable you to deduce
information from unstructured text data. Before you can apply different text mining
techniques, you must start with text preprocessing, which is the practice of cleaning and
transforming text data into a usable format. This practice is a core aspect of natural
language processing (NLP) and it usually involves the use of techniques such as
language identification, tokenization, part-of-speech tagging, chunking, and syntax
parsing to format data appropriately for analysis. When text preprocessing is complete,
you can apply text mining algorithms to derive insights from the data.

13. Text mining applications
Text analytics software has impacted the way that many industries work, allowing them to
improve product user experiences as well as make faster and better business decisions.

14. Some of these common text-mining
techniques include
● Customer service
● Risk management
● Maintenance
● Healthcare
● Spam filtering

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