Maybe you don't have a positive and a negative class.
Your input are word vectors. Unless you trained your word vectors before with explicit positive and negative labels, it is very unlikely that your KMeans learned that difference.
If you used pre-trained word vectors, your KMeans could have learned an arbitrary difference between cluster 0 and cluster 1. Maybe it learned which reviews are from males and which from females, maybe which have the word "parachute" and which don't have the word "parachute", the options are endless.
What you can do, is access which labels your KMeans learned (
model.labels_) and filter your input
X per cluster. Then, count the occurence of each word in each cluster and order which words happen the most in each of them. This might help you understand the difference between cluster 0 and cluster 1.
Note: if the top words you get are words like: a, the, of, if, etc. Use a stop-word list, or filter those word with a max document frequency threshold.