MaartenGr / MaartenGr/BERTopic
Best way to find all documents related to keyword
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- Dominant language
- Python
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Description
Say I have a topic model on a large collection of documents. What's the best way to find all topics and documents matching a certain keyword, eg the keyword "Sport" in the example topic model. I know I can search for similar topics, eg:
similar_topics, similarity = topic_model.find_topics("sport", top_n=20)
But I'm wondering if this is the best/only way? The semantic relationships in the 2D representation of topics isn't always that clear (as is often the case going from many-D to 2D), so I can't really just pick nearby topic clusters. What would you recommend as best practice here?
Many thanks.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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Research direction
Start with the shown topic_model.find_topics("sport", top_n=20) call and review how the 2D topic representation is used. Determine whether the existing topic model API can identify both matching topics and documents, and clarify the expected best-practice workflow before defining what completion means.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100