I have a yelp-review dataset. I have done a word2vector embedding on the text column of the yelp-review. I am using unsupervised leaning K-means and PCA & TSNE to visualise the data. I have got 6 clusters which are well separated. Now I want to create a "Word-Cloud" with respect to each of the cluster labels. Can one one give an idea how to do that. Thank you.
To answer your question correctly, it is necessary to understand the meaning of PCA axis.
The Principal Components are built according to linear correlations found in the multi dimentional vectors. It is difficult to give them a proper meaning, they are just correlated in a statistical point of view, but if you see their labels, you can see if they share a common field (for instance "guitar" and "piano" could be close to a principal component defining "sounds").
On the other hand, the more the cluster is concentrated, the more there is correlation between points, but you have to consider their distance from principal component axis.
For instance, if there is a concentrated cluster very close to PC2 axis positively or negatively,but far from PC1, it means that they are higly correlated together towards PC2, but no correlation with PC1.
Then, if 2 clusters are opposite with respect to 0, they are both anti correlated.
If 2 clusters make a 90° angle with respect to 0, they have no correlation.