What is SAS text Miner?
SAS® Enterprise Miner – SAS® Text Miner. Description: SAS® Enterprise Miner is a data mining tool that enables the development of descriptive and predictive modeling and in-database scoring. SAS® Text Miner allows the analysis of text data from the web, comment fields, books, and other text sources.
What is text mining used for?
Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data.
What are the disadvantages of text mining?
The greatest limitation on text mining has nothing to do with technology but with copyright legislation. The right to copy, digitise, and then to text mine is severely curtailed by the necessary restrictions placed on many texts to preserve the rights of copyright holders.
How much does SAS cost a year?
Entry costs to license the most basic package (SAS Analytics Pro) costs $8,700 (first year fee) at the SAS online store; this package includes Base SAS, SAS/STAT and SAS/Graph. SAS renewal fees generally run 25-30% of the first year fee.
Is SAS software paid?
SAS has more than 40 years of experience putting our customers first. Software updates are always free, and there’s never a cost for quality service from our technical support, unmatched for product and industry knowledge.
Is there a free version of SAS?
You can get free access to SAS OnDemand for Academics: Studio for learning purposes.
What is the difference between text mining and data mining?
While data mining handles structured data – highly formatted data such as in databases or ERP systems – text mining deals with unstructured textual data – text that is not pre-defined or organized in any way such as in social media feeds.
What are some dangers of using data mining?
Dangers of Data Mining
- Data Privacy. While data mining on its own doesn’t pose any ethical concerns, leaked data and unprotected data can cause data privacy concerns.
- Ethical Dilemmas.
- Inaccurate Data.
- Overvaluing the Output.