Based on prior knowledge, occupation can be a relevant feature in determining whether a person earns more than 50000 dollars per year. Some occupations generally have higher salaries compared to others, which makes it likely that certain occupations will be associated with higher income levels.

To analyze the relationship between occupation and the task, we can examine the distribution of occupations among those who earn more or less than 50000 dollars per year. Let's go through the dataset and group the occupation values for each target class.

Here is the analysis and the resulting dictionary:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Protective-serv", "?", "Other-service", "Craft-repair", "Adm-clerical", "Priv-house-serv", "Handlers-cleaners", "Armed-Forces"],
	"yes": ["Prof-specialty", "Exec-managerial", "Tech-support", "Sales", "Transport-moving"]
}
```

In the "no" list, we have included the following occupations: Machine-op-inspct, Farming-fishing, Protective-serv, ?, Other-service, Craft-repair, Adm-clerical, Priv-house-serv, Handlers-cleaners, and Armed-Forces. These occupations are generally associated with lower incomes.

In the "yes" list, we have included the following occupations: Prof-specialty, Exec-managerial, Tech-support, Sales, and Transport-moving. These occupations are commonly associated with higher incomes.

Note that for the "?" occupation, it may represent missing or unknown values, so it is included in the "no" list.

Please keep in mind that this analysis is based on general knowledge and assumptions. The associations between occupation and income may vary in different contexts and datasets.