snscrape VS contextualized-topic-models

Here's what I'm trying to use: https://github.com/JustAnotherArchivist/snscrapeWhat do I need to open/run any of this? My goal with this is to extract my follower list off Twitter, and I'd very much like to know how to run it on my machine instead of having someone run it for me on theirs. I can't even figure out what I need to open the Readme file.

snscrape? No idea if it would work on 32 bit Windows but worth a try https://github.com/JustAnotherArchivist/snscrape

Try Snscrape.

You might have some luck with snscrape: https://github.com/JustAnotherArchivist/snscrape

I was going to suggest twint, but that's more single user focused. Maybe snscrape works for you.

In our experiments, CTM works well with tweets: https://github.com/MilaNLProc/contextualized-topic-models (I'm one of the authors)

Use CTM https://github.com/MilaNLProc/contextualized-topic-models with sentiment labels to built distribution of words over labels

This library from MILA seems quite neat! I haven’t had the change to play with it though : https://github.com/MilaNLProc/contextualized-topic-models

a bit of shameless self-promotion, but we developed a topic model (https://github.com/MilaNLProc/contextualized-topic-models) that actually supports that use case!

If you use CTM, you can provide the topic model two inputs: the preprocessed texts (that will be used by the topic model to generate the topical words) and the unpreprocessed texts (to generate the contextualized representations that will be later concatenated to the document bag-of-word representation). We saw that this slightly improves the performance instead of providing BERT the already-preprocessed text. This feature is supported in the original implementation of CTM, not in OCTIS. See here: https://github.com/MilaNLProc/contextualized-topic-models#combined-topic-model

Cross-lingual Contextualized Topic Models with Zero-shot Learning from a team at MilaNLP which uses bag of words representations in combination with multi lingual embeddings from SBERT and works like a VAE (encode the input, use the encoded representation to decode back to a bag of words as close to the input as possible). Using SBERT embeddings makes their model generalise for other languages which may be useful. One major shortfall of this model as I understand is that it can't deal with long documents very elegantly - only up to BERT'S word limit (the workaround is to truncate and use the first words)