Based on my prior knowledge, the country of origin (native-country) can be a relevant feature in determining whether a person earns more than 50000 dollars per year. Some countries tend to have higher average incomes than others, so it is likely that there is a relationship between the native-country feature and the target variable.

To analyze this relationship, we can examine the distribution of native-country values for each target class separately and identify any patterns or differences.

Here is the dictionary based on the analysis:

```json
{
	"no": ["United-States", "?", "Mexico", "Guatemala", "Dominican-Republic", "El-Salvador", "Puerto-Rico", "Vietnam", "Columbia", "Haiti", "Nicaragua", "Peru"],
	"yes": ["United-States", "?", "India", "Cambodia", "Philippines", "Taiwan", "China", "Iran", "Japan", "Italy", "England", "Canada", "Germany", "South", "Scotland", "Hong", "Greece", "Yugoslavia", "Trinadad&Tobago", "France"]
}
```

From the analysis, we can see that the "no" class has a higher representation of countries like Mexico, Guatemala, Dominican-Republic, El-Salvador, Puerto-Rico, Vietnam, Colombia, Haiti, Nicaragua, and Peru. On the other hand, the "yes" class has a higher representation of countries like India, Cambodia, Philippines, Taiwan, China, Iran, Japan, Italy, England, Canada, Germany, South, Scotland, Hong, Greece, Yugoslavia, Trinadad&Tobago, and France.

Please note that the "?" value represents missing or unknown data and is included in both classes as it is hard to predict its impact on the target variable.