Doing cluster analysis on the text topics may not be a good idea if you want to understand the trend in topics. Cluster analysis would group your records and that means solving a different problem. If I understand your objective correctly, I think you can achieve it by creating user-defined topics rather than trying to track the system-generated topics. First run text topic node on your first set of comments (Day 1/Week 1/Month1), then look at the multi-term topics. From this you will get a business sense of what topics are generated. If they do not make sense, modify them and create them as user-defined topics. Say you end up with user-defined topics like, Topic 1: +big data,  +data, database, high, performance Topic 2: statistics, +models, visual, analytics, data Once you have your topics, for the remaining time periods, you will have to define user-defined topics same as the topics defined in day1/week1/month1 (as above). For each time period just look at the frequency of documents for each topic. That should give you a sense of how the topics trend over time. And I guess this a decent approach to start with. ... View more

I do not see any problem with the number of documents.  Depends on your objective. If you are trying to identify similarities between sub-sections, then write a small code to extract each sub-section and treat it as a single document. That will give you enough observations (documents): 25*35. If all your documents follow a standard template then you can easily use PERL regular expressions to extract each sub-section and create a new data set with sub-sections as records. ... View more