The sample content set contains comments submitted by customers to a  supermarket website. Among the comments are complaints about different  aspects of the products and services. The following are some aspects that we  might find relevant to finding and describing these complaints: Now that we have added various classes and properties to each word and token  appearing in the raw text, we need to look at the relationships between the words  and try to detect when a complaint is being expressed. Sentiment analysis  is a technique for identifying when a writer is expressing a  negative opinion about the subject. Negativity is often expressed in general  terms, using words such as “bad” and “disappointed”. Content Analytics provides  sentiment analysis, which is ready for immediate use, in the content analytics  miner. For information, see 6.10, “Sentiment view” on page 222. Here we take a  more domain-specific approach and limit ourselves to those words that might  reference the limited range of words in our custom dictionaries for the particular  domain. Some words have negative connotations only within a specific domain.  For example, the word “hair” in the beauty products domain is an important word  with no negativity. However, the same word in regard to food and food packaging  domain, it is almost exclusively negative. Note:  To walk through the example below, you need to have at least two  dictionaries compiled and added to the CustomerComments annotator:  Products and ProductDefects. If you do not have the entry  cake  (in the Products dictionary) and  moldy  (the  ProductDefects dictionary), substitute with your own entries. To find additional annotations, we start by doing the following analysis: It is before the expiration date, but the cake was moldy. GLYPH<SM590000> Comments that are negative GLYPH<SM590000> Product, service, or brand that is associated with complaints GLYPH<SM590000> Positive remarks about a competitor 1. In the test document, enter the following text: 2. Analyze this with the CustomerComments annotator. Among the annotations  are the two as shown in Figure 11-21. 3. Select the  Create Parsing Rule  tab.  Chapter 11. Customizing content analytics with IBM Content Analytics Studio  429 Figure 11-21 Two of the resulting annotations