Based on prior knowledge, we can analyze the relationship between the feature "job" and the task of whether the client subscribes to a term deposit.

To analyze this relationship, we can look at the distribution of job categories for clients who subscribed to a term deposit ("yes" class) and those who did not ("no" class).

Let's go through the data and identify the possible job categories for each target class:

For target class "yes":
- management
- technician
- entrepreneur
- blue-collar
- retired
- admin.
- services
- self-employed
- housemaid
- student

For target class "no":
- management
- technician
- entrepreneur
- blue-collar
- unknown
- retired
- admin.
- services
- self-employed
- unemployed
- housemaid
- student

Now, let's create the dictionary:

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

Please note that I included the "unknown" and "unemployed" job categories in the "no" class as these values are hard to predict and were part of the analysis.