Intro to KeyBERT - BERTopic for Topic Modeling

Key Takeaways

ah give it give it as a fun one um let me just quickly go to this one it's easier for those who want to visit the uh at the package uh keyboard is is super simple really really really basic um as a default essentially what we're doing is we want to extract keywords and why we're doing that is we create a documented branding from a document and then from each word in that document we use the same language model and create embeddings from those we do cosine similarity and pick the ones that are most similar that's it that's all we're doing um and the reason that that works so well is for two reasons uh language models tend to have um the same dimensional space with respect to both words and documents that work quite well especially when you then do cosine similarity or another distance measure the second thing is is the same with with your topic it's so intuitive there's not much going on there and then on top of that we can do some fine tuning so there's an algorithm called maximum marginal relevance where we can say okay I'm gonna throw out words that are very similar to each other I'm going to intro introduce some diversity of the keywords that I have and we can do things with the count vectorizer to only again use nouns or or verbs or whatever is interesting to your specific use case but the way it works like your topics really straightforward sometimes things don't have to be so complex