Based on prior knowledge, the relationship between the feature 'job' and the task of whether the client subscribed to a term deposit can be analyzed. Here is a possible analysis:

- There may be certain jobs that have a higher likelihood of a client subscribing to a term deposit, such as 'management' or 'retired'. These types of job roles typically have higher average income or stability, which may make them more likely to have disposable income for investment.
- Conversely, there may be job roles that have a lower likelihood of subscribing to a term deposit, such as 'unemployed' or 'student'. These types of job roles generally have lower or no income, which could make them less likely to have the means to invest in a term deposit.

Based on this analysis, let's create the dictionary:

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

Please note that these are only potential relationships based on the provided information, and there may be other factors influencing the target variable that are not captured by this feature alone.