Code demo of BERTopic - BERTopic for Topic Modeling

Key Takeaways

arcs of Articles right we've all read the articles here archive is is a really nice database with uh yeah before you go into it uh since you're showing code can you maybe zoom in a little bit just so the code is more it's not that clear right yeah yeah this is much better I think perfect um you know we want some data and uh what's nicer data then a lot of abstracts from Mostly computer vision and some machine learning articles and I randomly sample 10 000 of them just just to make it a little bit easier for myself and that's what we're going to do the topic modeling on this abstract for example is about reinforcement learning and so we have a lot of more of those abstracts that we want to see okay which articles or which topics become you know popular uh which topics can we find can we find some relationships between some of those topics can we do some fine tuning etc etc now we can train our model we can do the exact same thing as we did before we import our package we instantiate it and do fit transform on our abstract now I can't do that but I'm kind of lazy and we have limited time so I'm gonna load in the model instead I've done this save the model and now I'm essentially gonna load it in which I've done before so after this training procedure and this training procedures of course sentence Transformers uh umap hdb can scan CTF IDF we can view the topics that we have created so what we do is we have a topic model we do get topic info and what we get is a data frame that has several columns I have to call them topic that shows you the topic ID it goes from -1 to 147 so we see two things happening here there are a lot of topics that were found as a default but there's also that minus one and the minus one those are outliers I'm still showing the outliers because otherwise you feel like you're missing documents but those are all the documents that couldn't be clustered and we can fine tune it so it becomes larger or smaller but that depends on hdb scan we have the count of the number of documents in a topic but most interestingly we have the topic representation we have the topic ID and then words that are you know best representative of that cluster of the topic and if you pick this one that's reinforcement learning right and we have some object detection and we have some extra real attacks and we can go through all of these topics and see what exactly is happening here we can also say okay we're going to pick the top 10 10 most frequent ones and I'll read through them we see segmentation some molecular topics we can dive into those to see what exactly is being talked about something uh about Transformer models self-attention Visions interesting to see that also being there um we can go through all of these topics and and read to them but we can also say okay I don't like this representation I don't like the four here I don't like those underscores I think there are way too many words here so let's customize our topic labels now we can generate topic labels automatically based on the words that we have here but instead let's say we have three we remove that topic prefix because it's ugly or it's annoying or you know whatever the reason might be and we change the separator because at underscore might make it more difficult to read and one thing that we add on top of that let's define our own topics because I I think I know that this is about Transformer based models and I know that this one is about reinforcement learning so we can also do some topic labeling here now we've run this and we get exactly what we've done before right what we wanted to be doing we want to have this representation a little bit nicer than this one and we might be doing this because we want to visualize certain topics and you know we have some topics that are more interesting than others so we label them but you know you can also say I want 10 more to 20 words so it gives you a little bit more understanding of what is happening here and there are a bunch of more things that we can do with that topic model we can update the topics to you know change the anagram range because I want words concatenated in a way I want to merge a certain topic because I think they are very similar to one another and I don't want to consider them separately I might want to reduce the topics to 150 or 10. I might want to find certain topics that I couldn't find before because I don't want to go through 150 topics to see if my topic of interest is in there are many things that we can do with with something like this um that gives us a little bit more control over how our topic model looks like but after doing all of this we still want a little bit more knowledge about which topics are in there but also the relationship between topics or the relationship between documents and topics or the relationship between words within the topics etc etc and for that we come to the second pillar and that's the the visualizations that are possible with within their topic