Based on prior knowledge, we can analyze the relationship between the "occupation" feature and the target variable "Does this person earn more than 50000 dollars per year?". 

Some occupations are generally associated with higher incomes, such as "Exec-managerial" and "Prof-specialty", while others may have lower incomes, such as "Other-service" and "Priv-house-serv". We would expect individuals in managerial and professional occupations to have a higher likelihood of earning more than $50,000 per year compared to those in service-related occupations.

To complete the analysis, we'll go through the dataset and filter the occupation values for each target class separately.

Here's the resulting dictionary:

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

In the "no" class, we have a variety of occupations spanning different sectors, including machine operation/inspection, farming/fishing, protective services, other service-related jobs, craft repair, administrative clerical work, tech support, handling and cleaning, and armed forces.

In the "yes" class, we find occupations such as professional specialties, executive/managerial roles, sales positions, and transport/moving jobs.

Note that the occupation "?" is also included in the "no" class, indicating missing or unknown values.

Please keep in mind that this analysis is based on general trends and may not apply to every individual.