Based on my prior knowledge, the feature "previous" represents the number of contacts performed before this campaign and for this client. To analyze the relationship between this feature and the target variable (subscription to a term deposit), we can consider the following:

1. Clients with a higher number of previous contacts may have a higher likelihood of subscribing to a term deposit, as they already have a history of engagement.
2. On the other hand, clients with a lower number of previous contacts may have a lower likelihood of subscribing, as they may not be as familiar with the product or less interested.

To create the dictionary, we will examine typical values of the feature "previous" for each target class ('no' and 'yes'). Here's the analysis and dictionary:

```json
{
	"no": [0.0, 1.0, 2.0, 3.0, 4.0],
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```

In the 'no' class, typical previous values include 0.0, 1.0, 2.0, 3.0, and 4.0. Similarly, in the 'yes' class, typical previous values are 0.0, 1.0, 2.0, 3.0, and 4.0. These values represent a range of contacts performed before the campaign for both classes.