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Predictive Analytics II: Text, Web, and Social Media Analytics - Text Analytics and Text Mining Overview

Q: What is Text Analytics? How does it differ from text mining?

Text Analytics is the broader concept that includes: information retrieval, information extraction, data mining and web mining Text Mining: focuses on discovering new and useful knowledge from the textual data sources.

Q: What is text mining? How does it differ from data mining?

Text Mining: also text data mining of knowledge discovery in textual databases Text Mining is the same as data mining, with the same purpose and uses the same processes, but with unstructured text sources as input instead of structured data as with data mining. It can be thought of a 2 -step process : 1) impose structure on the text-based data sources, 2) extract relevant information and knowledge from this structured text-based data using data mining techniques and tools

Q: What are som of the most popular application areas of text mining?

Information extraction: identity key phrases and relationships Topic tracking : predict other documents of interest to the user Summarization Categorization : Identify Main themes and place the doc into a predefiend set of categories Clustering : group similar docs without having a predefined set of categories Concept linking : connect docs by identifying shared concepts and help users find information they would not have found (perhaps) Question answering : find the best answers to a given question knowledge-driven pattern matching

4 important questions on Predictive Analytics II: Text, Web, and Social Media Analytics - Text Analytics and Text Mining Overview

What is Text Analytics? How does it differ from text mining?

Text Analytics is the broader concept that includes:
information retrieval, information extraction, data mining and web mining
Text Mining: focuses on discovering new and useful knowledge from the textual data sources.

What is text mining? How does it differ from data mining?

Text Mining: also text data mining of knowledge discovery in textual databases
Text Mining is the same as data mining, with the same purpose and uses the same processes, but with unstructured text sources as input instead of structured data as with data mining.
It can be thought of a 2-step process: 1) impose structure on the text-based data sources, 2) extract relevant information and knowledge from this structured text-based data using data mining techniques and tools

Why is the popularity of text mining as an analytics tool increasing?

Unstructured data is increasing rapidly (doubling every 18 months); Knowledge is power in today's business world and knowledge is derived from data and information. Businesses that effectively and efficient tap into their text data sources will have a competitive advantage.

What are som of the most popular application areas of text mining?

Information extraction: identity key phrases and relationships
Topic tracking: predict other documents of interest to the user
Summarization
Categorization: Identify Main themes and place the doc into a predefiend set of categories
Clustering: group similar docs without having a predefined set of categories
Concept linking: connect docs by identifying shared concepts and help users find information they would not have found (perhaps)
Question answering: find the best answers to a given question knowledge-driven pattern matching