Finding best cluster after running K-Means clustering I have bunch of text which I want to segregate based on semantic similarity. Running through K-Means, I was able to divide the complete text into different clusters.

However, I still need to find which cluster contains most similar text. For e.g :-

Cluster 1 :-
'Hey there, how are you'.
'How are you dear'.
'I haven't seen you for past three days. How are you'.

Cluster 2 :-
'As per survey conducted, smoking kills more people than ...'.
'Avoid smoking in public places.'.
'People more likely to avoid smoking if they ...'.
'As per survey conducted, people most likely chose democrats'.

Cluster 1 should rank above Cluster 2 as it has more semantic similarity among text contained within.
Generally, library that you use for k-means clustering will report "within cluster sum-of-squares" value after running k-means procedure. A smaller value means the cluster is tightly bound (most messages are similar to each other). You can use this value to order the cluster IDs.