Based on prior knowledge, we can analyze the relationship between the "job" feature and the target variable "Does this client subscribe to a term deposit?"

Analyzing the correlation between the job and the target variable might give us insights into whether certain types of jobs are more likely to subscribe to a term deposit. Let's perform this analysis:

1. Calculate the percentage of clients subscribing to a term deposit in each job category.
2. Identify job categories that have a significantly higher or lower subscription rate compared to the overall subscription rate.

Here is the analysis of the relationship between the "job" feature and the target variable:

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

Based on the analysis, we can construct the dictionary as follows:

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

Please note that this analysis is based on a general assumption and may vary depending on the dataset and specific context.