Based on my prior knowledge, the feature "job" is likely to have some relationship with whether the client subscribes to a term deposit or not. Certain types of jobs may be more inclined towards subscribing to a term deposit, while others may not show a strong correlation.

To analyze the relationship between the feature "job" and the target variable, we can start by calculating the distribution of job types for both the "yes" and "no" classes of the target variable. This will give us an idea of the job types that are more common among clients who subscribe to a term deposit and those who do not.

Once we have the distribution, we can identify the job types that are prevalent in each class and create a dictionary with the format specified.

Here is the requested dictionary: 

```json
{
	"no": ["blue-collar", "services", "housemaid", "unknown", "unemployed", "student"],  
	"yes": ["management", "technician", "entrepreneur", "retired", "admin.", "self-employed"]
}
```

For the "no" class, common job types include blue-collar, services, housemaid, unknown, unemployed, and students. These job types are more frequently found among clients who do not subscribe to a term deposit.

For the "yes" class, common job types include management, technician, entrepreneur, retired, admin., and self-employed. These job types are more frequently found among clients who do subscribe to a term deposit.

Note: I have excluded job types with minimal representation or hard-to-predict correlations from the dictionary as advised.