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Figure 4-3 shows a collection of all the content analytics miner views. 

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Each view in the content analytics miner provides its own unique kind of insight from your content analysis. Analysis of distributions based on day of week and month of year in the Deviations view  often leads to interesting insights. You might be able to find unexpected patterns of weekend activities and seasonal differences. With the Trends view , you can also obtain actionable insights. If something undesirable is increasing, you might need to reverse the trend. The Facet Pairs view  unveils remarkable associations if you select the appropriate pairs of facets. Repeated selections of different facets to compare is crucial to your success. Content Analytics is designed to be interactive and scalable for repeated iterations through your data. 

To understand the content of the data, it is essential to read the original text with the Documents view . Some people misunderstand text mining  as something that allows you to avoid reading the actual documents. Even though text mining allows you to avoid reading the entire amount of textual data, it still requires you 

