corex_topic VS shifterator

We ended up settling on a HuggingFace transformer + HDBSCAN pipeline from BERTopic. I like this because it makes it straightforward to tune and test, and you probabilistically assign documents to clusters, so you can do interesting aggregation and sampling after you have your inference done, like selecting text. Other options include top2vec which basically does the same thing without some guiding tools available in BERTopic. Either is suitable for what you’re doing. Older techniques include things like the Latent Dirichlet Allocation and COREX.

If I understand correctly, it sounds like you are trying to do Topic Modeling on documents. One python library I’ve enjoyed using is https://github.com/gregversteeg/corex_topic which, besides doing general topic modeling, also learns hierarchies of topics.

We started off by trying LDA and NMF, but the topics were too messy so we wound up switching to CorEx (https://github.com/gregversteeg/corex_topic), which is a semi-supervised algo that lets you "nudge" the model in the right direction using anchor terms. By the time our topics started looking coherent, it turned out that a regex with the anchor terms we'd picked outperformed the model itself. This case study was on a relatively small sample of relatively short documents (~4k survey open-ends) but for what it's worth, we also tried to use topic models to classify congressional Facebook posts (much larger corpus and longer documents) and the results were the same.

Overfitting is certainly part of the problem - in one of my earlier posts I talk about "conceptually spurious words," which are essentially the product of overfitting - but the more difficult problem is polysemy. I'm sure there are ways to mitigate that - expanding the feature space with POS tagging, etc. - but ultimately I think the solution is to simply avoid using a dimensionality reduction method for text classification. Supervised models are clearly the way to go - even if those "models" are just keyword dictionaries curated based on domain knowledge.

Unsupervised is also possible for topic modelling: CorEX

You could use CorEX for topic modelling. With that you can find the type of causes and from there their distribution. Then you can join it with other non-textual meta-data.

This https://github.com/ryanjgallagher/shifterator