Based on prior knowledge, it is likely that the occupation of an individual is related to their income. Certain occupations, such as 'Exec-managerial' or 'Prof-specialty', may be associated with higher incomes, while others, like 'Other-service' or 'Priv-house-serv', may be associated with lower incomes.

To analyze the relationship between the occupation feature and the task of whether the person earns more than 50000 dollars per year, we can categorize and compare the occupation values for both the 'yes' and 'no' cases.

Here is the analysis and the resulting dictionary:

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

The 'no' category includes occupations like 'Machine-op-inspct,' 'Farming-fishing,' 'Other-service,' and 'Handlers-cleaners.' These occupations are more likely to have lower incomes.

The 'yes' category includes occupations like 'Protective-serv,' 'Prof-specialty,' 'Exec-managerial,' 'Tech-support,' 'Sales,' 'Craft-repair,' 'Adm-clerical,' and 'Transport-moving.' These occupations are more likely to have higher incomes.

Note: Occupations with missing values ('?') are excluded from the resulted dictionary.