Based on my prior knowledge, the feature "campaign" represents the number of contacts performed during the campaign for a particular client. To analyze the relationship between this feature and the target variable, which determines whether the client subscribes to a term deposit or not, we can consider the following steps:

1. Categorize the target variable: We need to categorize the target variable into the classes "yes" and "no" based on whether the client subscribed to a term deposit or not.

2. Analyze typical campaign values for each target class: We will look at typical campaign values for both the "yes" and "no" classes to understand the relationship between the feature and the target.

3. Generate the dictionary: We will create a dictionary with the "no" and "yes" classes as keys and lists of campaign values as the corresponding values.

Here is the generated dictionary:

```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

Please note that the specific values 1.0, 2.0, 3.0, 4.0, and 5.0 are just examples of typical campaign values and may vary based on the dataset used for analysis.