Topic Reduction

BERTopic uses HDBSCAN for clustering the data and it cannot specify the number of clusters you would want. To a certain extent, this is an advantage, as we can trust HDBSCAN to be better in finding the number of clusters than we are. Instead, we can try to reduce the number of topics that have been created. Below, you will find three methods of doing so.

Warning

For all cases of topic reduction it is generally advised to create the number of topics you would first through the clustering algorithm. That tends to be the most stable technique and often gives you the best results. This also applies with algorithms that do not allow you to select the number of topics beforehands, like HDBSCAN where you can make sure of the min_cluster_size parameter to control the number of topics. Therefore, it is highly advised to not use nr_topics before you have attempted to control the number of topics through the clustering algorithm!

Manual Topic Reduction

Each resulting topic has its feature vector constructed from c-TF-IDF. Using those feature vectors, we can find the most similar topics and merge them. Using sklearn.cluster.AgglomerativeClustering, the resulting feature vectors are clustered to get to the set value of nr_topics by finding out which topics are most similar to one another through cosine similarity.

To do so, you can make sure of the nr_topics parameter:

from bertopic import BERTopic
topic_model = BERTopic(nr_topics=20)

It is also possible to manually select certain topics that you believe should be merged. For example, if topic 1 is 1_space_launch_moon_nasa and topic 2 is 2_spacecraft_solar_space_orbit it might make sense to merge those two topics:

topics_to_merge = [1, 2]
topic_model.merge_topics(docs, topics_to_merge)

If you have several groups of topics you want to merge, create a list of lists instead:

topics_to_merge = [[1, 2]
                   [3, 4]]
topic_model.merge_topics(docs, topics_to_merge)

Automatic Topic Reduction

One issue with the approach above is that it will merge topics regardless of whether they are very similar. They are simply the most similar out of all options. This can be resolved by reducing the number of topics automatically. To do this, we can use HDBSCAN to cluster our topics using each c-TF-IDF representation. Then, we merge topics that are clustered together. Another benefit of HDBSCAN is that it generates outliers. These outliers prevent topics from being merged if no other topics are similar.

To use this option, we simply set nr_topics to "auto":

from bertopic import BERTopic
topic_model = BERTopic(nr_topics="auto")

Topic Reduction after Training

Finally, we can also reduce the number of topics after having trained a BERTopic model. The advantage of doing so is that you can decide the number of topics after knowing how many are created. It is difficult to predict before training your model how many topics that are in your documents and how many will be extracted. Instead, we can decide afterward how many topics seem realistic:

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

# Create topics -> Typically over 50 topics
docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)

# Further reduce topics
topic_model.reduce_topics(docs, nr_topics=30)

# Access updated topics
topics = topic_model.topics_

The reasoning for putting docs as a parameter is that the documents are not saved within BERTopic on purpose. If you were to have a million documents, it is very inefficient to save those in BERTopic instead of a dedicated database.