Also note that the set of components are unique up to sign if the data are centered (which they should be). In other words, you can get the same scores solution using a rotation matrix of
[-1.0, 0.5, 0.3,
2.1, -0.1, 1.1,
-3.5, 9.1, 0.1]
as you would with
[ 1.0, -0.5, -0.3,
-2.1, 0.1, -1.1,
3.5, -9.1, -0.1]
This means that you can't really rely on the sign of a single component's loadings to make comments about direction. You can still draw conclusions from magnitude and similarity. For example, if two predictors have almost identical values in a loading vector, they are likely to have a high correlation.