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BERTopic

BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.

BERTopic supports guided, supervised, semi-supervised, manual, long-document, hierarchical, class-based, dynamic, and online topic modeling. It even supports visualizations similar to LDAvis!

Corresponding medium posts can be found here, here and here. For a more detailed overview, you can read the paper or see a brief overview.

Installation

Installation, with sentence-transformers, can be done using pypi:

pip install bertopic

You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:

pip install bertopic[flair]
pip install bertopic[gensim]
pip install bertopic[spacy]
pip install bertopic[use]

Quick Start

We start by extracting topics from the well-known 20 newsgroups dataset containing English documents:

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']

topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)

After generating topics and their probabilities, we can access the frequent topics that were generated:

>>> topic_model.get_topic_info()

Topic   Count   Name
-1      4630    -1_can_your_will_any
0       693     49_windows_drive_dos_file
1       466     32_jesus_bible_christian_faith
2       441     2_space_launch_orbit_lunar
3       381     22_key_encryption_keys_encrypted

-1 refers to all outliers and should typically be ignored. Next, let's take a look at the most frequent topic that was generated, topic 0:

>>> topic_model.get_topic(0)

[('windows', 0.006152228076250982),
 ('drive', 0.004982897610645755),
 ('dos', 0.004845038866360651),
 ('file', 0.004140142872194834),
 ('disk', 0.004131678774810884),
 ('mac', 0.003624848635985097),
 ('memory', 0.0034840976976789903),
 ('software', 0.0034415334250699077),
 ('email', 0.0034239554442333257),
 ('pc', 0.003047105930670237)]

Using .get_document_info, we can also extract information on a document level, such as their corresponding topics, probabilities, whether they are representative documents for a topic, etc.:

>>> topic_model.get_document_info(docs)

Document                               Topic    Name                        Top_n_words                     Probability    ...
I am sure some bashers of Pens...       0       0_game_team_games_season    game - team - games...          0.200010       ...
My brother is in the market for...      -1     -1_can_your_will_any         can - your - will...            0.420668       ...
Finally you said what you dream...      -1     -1_can_your_will_any         can - your - will...            0.807259       ...
Think! It is the SCSI card doing...     49     49_windows_drive_dos_file    windows - drive - docs...       0.071746       ...
1) I have an old Jasmine drive...       49     49_windows_drive_dos_file    windows - drive - docs...       0.038983       ...

NOTE: Use BERTopic(language="multilingual") to select a model that supports 50+ languages.

Modularity

By default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence. However, it assumes some independence between these steps which makes BERTopic quite modular. In other words, BERTopic not only allows you to build your own topic model but to explore several topic modeling techniques on top of your customized topic model:

You can swap out any of these models or even remove them entirely. Starting with the embedding step, you can find out how to do this here and more about the underlying algorithm and assumptions here.

Overview

BERTopic has many functions that quickly can become overwhelming. To alleviate this issue, you will find an overview of all methods and a short description of its purpose.

Common

Below, you will find an overview of common functions in BERTopic.

Method Code
Fit the model .fit(docs)
Fit the model and predict documents .fit_transform(docs)
Predict new documents .transform([new_doc])
Access single topic .get_topic(topic=12)
Access all topics .get_topics()
Get topic freq .get_topic_freq()
Get all topic information .get_topic_info()
Get all document information .get_document_info(docs)
Get representative docs per topic .get_representative_docs()
Update topic representation .update_topics(docs, n_gram_range=(1, 3))
Generate topic labels .generate_topic_labels()
Set topic labels .set_topic_labels(my_custom_labels)
Merge topics .merge_topics(docs, topics_to_merge)
Reduce nr of topics .reduce_topics(docs, nr_topics=30)
Reduce outliers .reduce_outliers(docs, topics)
Find topics .find_topics("vehicle")
Save model .save("my_model")
Load model BERTopic.load("my_model")
Get parameters .get_params()

Attributes

After having trained your BERTopic model, several are saved within your model. These attributes, in part, refer to how model information is stored on an estimator during fitting. The attributes that you see below all end in _ and are public attributes that can be used to access model information.

Attribute Description
topics_ The topics that are generated for each document after training or updating the topic model.
probabilities_ The probabilities that are generated for each document if HDBSCAN is used.
topic_sizes_ The size of each topic
topic_mapper_ A class for tracking topics and their mappings anytime they are merged/reduced.
topic_representations_ The top n terms per topic and their respective c-TF-IDF values.
c_tf_idf_ The topic-term matrix as calculated through c-TF-IDF.
topic_labels_ The default labels for each topic.
custom_labels_ Custom labels for each topic as generated through .set_topic_labels.
topic_embeddings_ The embeddings for each topic if embedding_model was used.
representative_docs_ The representative documents for each topic if HDBSCAN is used.

Variations

There are many different use cases in which topic modeling can be used. As such, several variations of BERTopic have been developed such that one package can be used across many use cases.

Method Code
Topic Distribution Approximation .approximate_distribution(docs)
Online Topic Modeling .partial_fit(doc)
Semi-supervised Topic Modeling .fit(docs, y=y)
Supervised Topic Modeling .fit(docs, y=y)
Manual Topic Modeling .fit(docs, y=y)
Topic Modeling per Class .topics_per_class(docs, classes)
Dynamic Topic Modeling .topics_over_time(docs, timestamps)
Hierarchical Topic Modeling .hierarchical_topics(docs)
Guided Topic Modeling BERTopic(seed_topic_list=seed_topic_list)

Visualizations

Evaluating topic models can be rather difficult due to the somewhat subjective nature of evaluation. Visualizing different aspects of the topic model helps in understanding the model and makes it easier to tweak the model to your liking.

Method Code
Visualize Topics .visualize_topics()
Visualize Documents .visualize_documents()
Visualize Document Hierarchy .visualize_hierarchical_documents()
Visualize Topic Hierarchy .visualize_hierarchy()
Visualize Topic Tree .get_topic_tree(hierarchical_topics)
Visualize Topic Terms .visualize_barchart()
Visualize Topic Similarity .visualize_heatmap()
Visualize Term Score Decline .visualize_term_rank()
Visualize Topic Probability Distribution .visualize_distribution(probs[0])
Visualize Topics over Time .visualize_topics_over_time(topics_over_time)
Visualize Topics per Class .visualize_topics_per_class(topics_per_class)

Citation

To cite the BERTopic paper, please use the following bibtex reference:

@article{grootendorst2022bertopic,
  title={BERTopic: Neural topic modeling with a class-based TF-IDF procedure},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
  year={2022}
}
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