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Text Mining & Knime Demonstration
Transcript of Text Mining & Knime Demonstration
Most times the word text analytics is used to describe text mining
But what is text analytics?
Analytic is the discover and communication of meaningful patterns in data.
Well that type of data is of course text
Text Mining Methods
Twitter is an example of a text mining application but what kind of methods does that application use
Knime is a software that allows you to do text mining in various types of ways depending on what algorithm and what type of results the text miner is looking for
Why Do We Need Text Miners?
Text mining first surfaced in the
Eventually technological advances have enabled the field to advance during the past decade.
Text mining is an interdisciplinary field of course. It not only deals with data mining it also deals with statistics and computational linguistics.
There is a huge need for the research in the text mining field and also the need for more text miners. Because most information today is currently stored as text which is unstructured and hard to process.
Where is text mining applications today?
Text mining is used in our every day in most application that deal with analyzing a business, searching, and even social networking
Of course is a type of programming language
It is used for statistical analysis
It is widely used among data miners and statisticians
Combining R programming and Knime makes a great analysis of a database of text
How does Knime work
I will demonstrate how a workflow looks and how to use it for your future experiments and research.
SIT BACK AND ENJOY!
Text Mining is changing history and more and more researchers are starting use text mining
Why Does Text Mining Matter?
Twitter is a great example of a text mining application.
Trending Topic on twitter uses a text mining application to show how many people have talked about one topic
It has made Twitter a very popular social network because everyone wants to know what everyone else is talking about
The Different Methods of Text Mining
Here is a few common methods
Twitter and Google among other companies use text categorization
When searching on twitter or Google you can find the search results in other languages
DR. C. L. CHEN
MY MATH PROFESSORS
YOU ALL FOR JOINING ME ON THIS PRESENTATION
Text categorization or text classification is the assignment of natural language documents to PREDEFINED categories according to their content.
Example: Information retrieval in libraries
Language identification is a particular application of text categorization.
Unlike text categorization, document clustering has no predefined category. The clustering algorithms figures out how well the document fits into each cluster.
Example: This creates links between similar documents, which allow another document to be added to the link if it is deemed relevant.
Concept/Entity Extraction is identifying key phrases, tags, entities and/or relationships with in text
Example: My research results will show entity extraction