6C. Multiple Representations

During the development of BERTopic, many different types of representations can be created, from keywords and phrases to summaries and custom labels. There is a variety of techniques that one can choose from to represent a topic. As such, there are a number of interesting and creative ways one can summarize topics. A topic is more than just a single representation.

Therefore, multi-aspect topic modeling is introduced! During the .fit or .fit_transform stages, you can now get multiple representations of a single topic. In practice, it works by generating and storing all kinds of different topic representations (see image below).

The approach is rather straightforward. We might want to represent our topics using a PartOfSpeech representation model but we might also want to try out KeyBERTInspired and compare those representation models. We can do this as follows:

from bertopic.representation import KeyBERTInspired
from bertopic.representation import PartOfSpeech
from bertopic.representation import MaximalMarginalRelevance
from sklearn.datasets import fetch_20newsgroups

# Documents to train on
docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']

# The main representation of a topic
main_representation = KeyBERTInspired()

# Additional ways of representing a topic
aspect_model1 = PartOfSpeech("en_core_web_sm")
aspect_model2 = [KeyBERTInspired(top_n_words=30), MaximalMarginalRelevance(diversity=.5)]

# Add all models together to be run in a single `fit`
representation_model = {
   "Main": main_representation,
   "Aspect1":  aspect_model1,
   "Aspect2":  aspect_model2 
}
topic_model = BERTopic(representation_model=representation_model).fit(docs)

As show above, to perform multi-aspect topic modeling, we make sure that representation_model is a dictionary where each representation model pipeline is defined. The main pipeline, that is used in most visualization options, is defined with the "Main" key. All other aspects can be defined however you want. In the example above, the two additional aspects that we are interested in are defined as "Aspect1" and "Aspect2".

After we have fitted our model, we can access all representations with topic_model.get_topic_info():

As you can see, there are a number of different representations for our topics that we can inspect. All aspects are found in topic_model.topic_aspects_.