semi-supervised topic modelling with multiple labels per document

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Hi there,

I have a dataset with 2000 participants who reported their most negative daily event for 90 days. They completed three questions related to their most negative event:
(1) an open-ended written question "what was you most negative event today?
(2) which categories does this event belong to (select all that apply) (e.g., mental health, physical health, relationship with family, etc)
(3) how negative was this event (i.e., 7-point likert with 1 - not at all negative and 7 - very negative)

The known topic categories are rather coarse, so the aim of using BERTopic is find a more fine-grained understanding topics participants' wrote about. From https://github.com/MaartenGr/BERTopic/issues/826#issuecomment-1306746031 I understand that the semi-supervised labelling does not support multiple labels.

I have also read https://github.com/MaartenGr/BERTopic/issues/1725#issuecomment-1879975094. However I'm not sure if there are alternatives to the suggestions provided there.

In a nutshell, I'm wondering what would be the best way to combine the above features (written response, known categories, and participant ratings) to improve the performance of BERTopic in finding topics. Any help in how to proceed would be greatly appreciated!

Thanks,
Justin

Start by reviewing the discussion in issues 826 and 1725, especially the referenced comment and proposed alternatives. Compare those approaches against the three stated inputs—written responses, multiple known categories, and participant ratings—and document whether BERTopic supports a practical combined workflow or what feature would be needed.

Written by the indexing model from the issue text.