text. Techniques such as custom dictionaries and syntax rules can help extract this valuable data from free-form text, and make it available in a facet for new ways to explore your data set. 

The next section describes several scenarios, explaining how you can define a dictionary or custom text analysis rules with the sample data to potentially discover additional insights from your data. 

## 7.2 Content analysis scenarios for the sample collection 

To better help you understand how to perform content analysis, this section includes the following scenarios, which include use of the content analytics miner views, and iterative improvement of facets: 

GLYPH<SM590000> Scenario 1: Using a custom dictionary to discover package- related calls 

GLYPH<SM590000> Scenario 2: Using custom text analysis rules to discover trouble-related calls 

GLYPH<SM590000> Scenario 3: Discovering the cause of increasing calls 

For the procedures to create the associated custom dictionary and custom text analysis rules, see 8.2.2, “Configuring the Dictionary Lookup annotator” on page 282, and 8.3, “Configuring the Pattern Matcher annotator” on page 291. 

Before you proceed with the scenarios, see Chapter 3, “Designing content analytics solutions” on page 41 and Chapter 4, “Understanding content analysis” on page 59, if you have not already done so. 

## 7.2.1 Scenario 1: Using a custom dictionary to discover package- related calls 

This scenario explains how to use a custom dictionary to discover package-related calls to the call center. The Facet Pairs view is used for this scenario. 

Though the sample collection does include problem category and subcategory facets, we will imagine (for the sake of this tutorial) that this data does not initially exist, and that we need to somehow create it by looking for package-related words in our collection. 

Tip for finding candidates for a dictionary entry or rule:  The best resource for finding words and patterns is your collection data. The predefined Part of Speech and Phrase Constituent facets provide good candidates. The Terms of Interest facet also shows a candidate list if it is enabled. 

