Building a separate LDA model per week means the algorithm's definition of different topics will change from week-to-week. That raises the thorny question of whether particular distinct per-week topics are in any way overlaps or evolutions of previous topics.
Such a comparison might be possible, but seems more complicated than an alternative approach: train your LDA model on the entire corpus, without segmentation by weeks. Then you have one model for "the whole discourse".
You could then provide the more-simple analysis of comparing how each week's articles reflect a different mix of topics. For example, does a single topic become more or less prevalent over time?
Im working on Topic Modeling on Twitter Data. I extracted the data and stored it in MySQL table. The columns are Date, Place, UserID, Text, tweetID, likes, weekID(based on date I have assigned what week it belongs to).
I have also taken data of each week and built a LDA model for each week. Im currently using pyLDAvis from Gensim to visualize the topics in each week.
Is there any way I can compare the LDA models which I have for each week. I want to compare them so I can see how a specific topic has been changing over the weeks.
Any ideas is much appreciated.
I have tried to build LDA models of each week and I have saved them into html and LDA model files. I want to see how topics have been changing between the weeks.
Compare two LDA models