As umm... Mr. Bonferroni notes, his correction works on any p-values, regardless of their source. However, there are other procedures, like the Holm's, which are uniformly more powerful; subject to certain other restrictions, like positive dependence between the tests, other methods are even more powerful still. Sorry, Carlo.
These corrections are intended to preserve the familywise error rate, which essentially means that you want to keep the probability of making one or more errors within a "family" of tests at the same level you would accept for a single test. To do this intelligently, you need to define the families appropriately. Based on your description, it sounds like you have at least two families of tests: the first consisting of mood data and the 2nd consisting of the self-report scales. For example, you might be testing whether some manipulation causes subjects in the treatment group (vs. appropriate controls) to 1) experience a change in mood and 2) be aware of said change.
Accordingly, I'd consider adjusting the first set with $n=6$ and the latter with $n=4$. Since the one of the self-report values is the overall scale, you could potentially argue that it "protects" the three sub-scores too, so perhaps I'd consider reporting the overall score (uncorrected) and three sub-scores corrected with $n=3$, especially if the overall test is significant. If the Likert (or self-report) scales all attempt to measure the same thing, I'd be tempted to apply an omnibus test (e.g., an ANOVA) first, which might give you more power.
However, I think applying the multiple comparisons to both sets of data (with $n=10$ is over-conservative) unless these tests are intimately linked.
