K-means clustering of word embedding gives strange results
Word embeddings are trained by substitutability, not similarity.

If you consider a sentence like "This food is unflavored." Then a good substitute word would be "flavored", and the sentence will still be "correct".

In many cases, substitutability arises from similarity (crunchy, crispy) but it does also arise from opposites. You may consider "king" and "queen" to be opposites, too.

You probably should use a supervised approach then.