Error Analysis for misclassification of text documents
It seems to me that both of your questions could be answered by storing the retrieved neighbours on your test set and giving them a thorough analysis. Assuming you are using a unigram + tf-idf text representation and a cosine similarity distance metric for your K-NN retrieval, it would be trivial once you have a classified document to display the K neighbours and analyze their common unigrams and their respective tf-idf weights in order to see what influenced the classification. Moreover, doing it on your misclassified documents could help you understand which features caused the error.

I'd be interested to know if there is a more systematized approach to those issues.