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With text analytics, computers parse text for data instead of reading it.
Developers have created any number of text analytics algorithms that do different things to text in order to collect information about the text. For example, text analytics has been very important in natural language processing, where pioneering developments try to help computers understand human speech.
One way to talk about some text analytics programs is that they 'take semantics out’ of text. Semantic labeling is part of what allows humans to read the text naturally. Taking out semantics can leave a more structured result that is easier for a computer to analyze.
For a deeper look at text mining or text analytics programs, it can be helpful to look at the difference between semantic types of programming languages, which humans write in, and machine language, which is what computers read. That’s the concept at work in text analytics: for example, a text analytics program can go through text and assign numbers to specific words and phrases in order to provide a computed result for use in an analytics interface, which is then interpreted by human users.