I have run different topic modeling approaches on my data (clinical data related to Cognitive impairment diseases). we are going to process what is important that makes it develop to a harsher disease. First, I have divided my data into different 6-month data (from a starting point back every 6 months) and then run the topic modeling approach on every 6 months. I was going to see the difference between the derived topics of each 6 months.
For example, for the first six months there are 20 topics, then for the second six month there 20 topics and ... till the tenth six month (5 years). I was hopeful to see a different topic in every six months or at least each year. but sadly most of the words have been repeated in every 6 months. However, the number of the words has changed.
For example in the first six months word "sleeping" has been repeated 10 times in different topics but in the second 6 months, it has been repeated 4 times.
What I am going to say is that, if we look at this as a thing that times matters, I can not see any pattern visibly in my data unless I rely on the number of words changing in every six months.
Do you think analyzing my output and plotting the different words number in different 6 months makes sense at all? Or is that unreliable?
Also, do you mind recommending other approaches that I can apply to get insight out of the output of my topic modeling (please consider that the changing in each six months matters)?
I greatly appreciate your help.