what actually word embedding dimensions values represent?
These columns are actually arbitrary, they do not represent anything for humans. However, it does not mean they are useless, quite the opposite - computers can extract features from this highly dimensional space more easily, such as in neural networks.

To extract information which is useful for humans, I recommend to have a look at examples in original paper by Tomas Mikolov:

We can have a look at common capital city.  Athens is to Greece (in sense that they have similar relationship) as is Oslo to Norway.
We can use afterwards vectors obtained using GloVe and construct

$$v(Athens) - v(Greece) + v(Norway)$$

and this vector in 'globally' (meaning that trained on universal dataset, such as wikipedia) trained model would be closest to $v(Oslo)$.