Based on my analysis, I will analyze the relationship between the "job" feature and the task of whether the client subscribes to a term deposit or not.

Thorough analysis:

The "job" feature represents the type of job the client has. It is a categorical variable with several categories such as 'management', 'technician', 'entrepreneur', 'blue-collar', 'unknown', 'retired', 'admin.', 'services', 'self-employed', 'unemployed', 'housemaid', and 'student'.

To determine the relationship between this feature and the target variable, we need to examine the distribution of job categories for clients who do and do not subscribe to a term deposit. This will help us understand if certain job categories are more likely to result in a subscription.

Once we have analyzed the relationship, we can create a dictionary in the specified format.

Here is the dictionary with the specific details:

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

In this case, I have included all possible values of the "job" feature for both the "no" and "yes" target classes. Since there is no specific information indicating that certain job categories are more likely to result in a subscription, I have included all categories in both lists.

Please note that if the analysis had shown that certain job categories were strongly associated with one target class but not the other, I would have included only those categories in the respective list.