Common Mistakes in Text Analytics of Consumer Reviews

By Jeff Catlin Co-Founder and CEO, Lexalytics, Inc | November 29, 2015

Let's start with a basic primer on text analytics (aka text mining). Depending on whom you ask, the two are different, but in my experience, it really comes down to what industry you are coming from. This is one of the more annoying things about the "natural language processing" industry – the terminology hasn't settled down quite yet.

Since they're close enough, let's just use "text analytics" for purposes of this article.

To quote Wikipedia : The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.

Or, as we like to put it, "text analytics" tells you "who, what, where, when, and (sometimes) why" – so that you can figure out what you need to do about it.

Text analytics is a very powerful tool, and can be transformational for a business. However, there are a number of very common mistakes that we see again and again, and we'd like to help you keep from making those mistakes.

What We See People Doing Wrong

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