I have run different topic modeling approach on my data(its clinical data related to Cognitive impairment diseases. we are going to process what thing is important that make it develop to more harsh disease). before anything, 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 (5 years). I was hopeful to see a different topic because of the use case I have in every six months or at least each 1 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 its something unreliable.
Also, do you mind to let me know what other approach is there 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)?