This can be explained by the fact that the corpora used to train the Amazon embeddings contain many more unlabeled reviews for books and electronics (973,194 / 122,438 respectively) than electronics and kitchen (21,009 / 17,856).
the vector representations for sentiment words that only appear in the books and DVD subcorpora are of higher quality than those that only appear in the electronics and kitchen subcorpora (see Table 4).