Based on my prior knowledge, the feature 'campaign' represents the number of contacts performed during a campaign for a particular client. To analyze the relationship between this feature and the task of whether the client subscribes to a term deposit or not, we can look at the distribution of campaign values for each target class.

Here is the analysis and 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]
}
```

The values in the "no" list represent typical campaign values for the target class 'no', indicating that clients who did not subscribe to a term deposit had campaigns with 1, 2, 3, 4, and 5 contacts performed during the campaign. The same set of values can be observed for the "yes" list, representing typical campaign values for the target class 'yes', meaning clients who subscribed to a term deposit also had campaigns with 1, 2, 3, 4, and 5 contacts performed during the campaign.

Please note that these values are approximate and can be modified based on specific data and domain knowledge.