SAS text analytics provides a flexible framework capable of tackling a variety of use cases in support of a single initiative. This framework supports the end-to-end text analytics life cycle, including preparing data, visually exploring topics, extracting entities and facts, analyzing sentiment, building a variety of text models and deploying them within existing systems or processes. Machine learning powers topic generation, categorization, entity/fact and sentiment extraction to automatically identify relationships and patterns that exist within text.
The SAS Platform fosters collaboration by providing a toolbox where best practice pipelines and methods can be shared. SAS also seamlessly integrates with existing systems and open source technology. Open APIs enable users to call SAS text analytics using a programming environment and language of familiarity. APIs are available for Java, Python, Lua, R and RESTful web services.
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