BERTopic VS scattertext

Try experimenting with different hyperparameters, clustering algorithms and embedding representations. Try https://github.com/MaartenGr/BERTopic/tree/master/bertopic

Try out this notebook which comes with the BERTopic repository.

After some googling, I found this (but for M1 chip Mac) --I wonder if I'm stuck. Is this laptop just not up for the job of working with sentence transformers? Appreciate your advice

Most has already been said and I am not sure how relevant this is but since you are focusing on human raters it might be worthwhile to mention that there is a Pull Request in BERTopic that allows you to use models on top of the default pipeline that further fine-tunes the topic representation. In theory, this would allow you to even use ChatGPT or any of the other OpenAI models to label the topics. From a human annotator perspective, this might be interesting to pursue.

The BERTopic library allows you to plug and play any type of embedding.

A number of excellent topic modeling libraries exist in Python today. BERTopic and Top2Vec are two of the most popular. Both use sentence-transformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.

BERTopic is a library to consider if you want something that groups data by topic.

I have experimented a bit with BERTopic but didn't find the results very useful. The issue is, that it is very important what exactly people are liking or disliking about the products, not just the fact that they are talking about specific aspects.

If what you want is to determine how similar two categories are, or to learn something about the structure or words that compose those categories, you might consider word shift graphs or Scattertext.

Other suggestions: Take a look at Scattertext. Compare keywords to the problem of aspect extraction. I think an underutilized way to look at textual data when you have a single group of interest is the word-frequency-based odds ratio.