(semi)-Supervised Topic Modeling
In this tutorial, we will be looking at a new feature of BERTopic, namely (semi)-supervised topic modeling! This allows us to steer the dimensionality reduction of the embeddings into a space that closely follows any labels you might already have. In other words, we use a semi-supervised UMAP instance to reduce the dimensionality of embeddings before clustering the documents with HDBSCAN.
First, let us prepare the data needed for our topic model:
from bertopic import BERTopic from sklearn.datasets import fetch_20newsgroups data = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes')) docs = data["data"] categories = data["target"] category_names = data["target_names"]
We are using the popular 20 Newsgroups dataset which contains roughly 18000 newsgroups posts that each is
assigned to one of 20 categories. Using this dataset we can try to extract its corresponding topic model whilst
taking its underlying categories into account. These categories are here the variable
Each document can be put into one of the following categories:
>>> category_names ['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']
Semi-supervised Topic Modeling¶
In semi-supervised topic modeling, we only have some labels for our documents. The documents for which we do have labels are used to somewhat guide BERTopic to the extraction of topics for those labels. The documents for which we do not have labels are assigned a -1. For this example, imagine we only the labels of categories that are related to computers and we want to create a topic model using semi-supervised modeling:
labels_to_add = ['comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x',] indices = [category_names.index(label) for label in labels_to_add] y = [label if label in indices else -1 for label in categories]
y variable contains many -1 values since we do not know all the categories.
Next, we use those newly constructed labels to again BERTopic semi-supervised:
topic_model = BERTopic(verbose=True).fit(docs, y=y)
And that is it! By defining certain classes for our documents, we can steer the topic modeling towards modeling the pre-defined categories.
Supervised Topic Modeling¶
In supervised topic modeling, we have labels for all our documents. This can be pre-defined topics or simply documents
that you feel belong together regardless of their content. BERTopic will nudge the creation of topics towards these categories using the pre-defined labels.
To perform supervised topic modeling, we simply use all categories:
topic_model = BERTopic(verbose=True).fit(docs, y=categories)
The topic model will be much more attuned to the categories that were defined previously. However, this does not mean that only topics for these categories will be found. BERTopic is likely to find more specific topics in those you have already defined. This allows you to discover previously unknown topics!