Over the last 10 years text analytics has become quite popular witnessed by the numerous offerings from commercial companies and open source libraries, for automatic information extraction, sentiment analysis, relation extraction, to name a few applications. Many of these products make bold claims about their high accuracy and impressive ability to tackle the most difficult challenges in the analysis of human language (polysemy, entity resolution, sarcasm, etc.). Their use of buzz words like AI, NLP, deep semantic, gives them an aura of scientific credibility, yet users who dare to look closely are often disappointed by the performance. In this presentation, we will discuss why human language represents such a challenge for data analysts. We will look inside the black box of some text analytics techniques to get a better understanding of the main challenges that still need to be solved. We will also illustrate some successful applications to help the audience appreciate the true value text analytics can offer. We will go behind the curtain to show you what is questionable so that you can establish realistic expectations and appreciate the real power and potential of text analytics.