WordNet focuses mostly on taxonomic relations rather than semantic ones. However, this is not necessarily a shortcoming of WordNet, since the qualia of "awesomeness" are very, very nuanced. Moreover, how do you know that This camera is awesome is in fact positive?-- what if the whole statement is:
This camera is awesome: First, the flash died on my wedding night --- the day after I bought it --- and then the battery caught fire and burnt my wedding dress, which was hand-sewn by my great-great grandmother.
However, one usage of WordNet for inferring semantic orientation was successful (at least in certain tasks): The work done by Kamps et al. in 2004. They based their orientation metric on the geodesic distance of a given word (e.g. awesome) from both good and bad:
EVA(w) = (d(w,bad)−d(w,good))/d(good,bad)
They claim that in certain tasks with certain data, this can work quite well, but its success is nevertheless very dependent on the task at hand; Looking into their work and work related to it may be a good place to start looking for answers.