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. It even supports visualizations similar to LDAvis!

Corresponding medium post can be found here and here.

Installation

Installation can be done using a local installation as the custom package is not published.

• Clone the topic modelling repo locally and move the csv filesdownloaded from PhantomBuster in the folder: topic-modeling/data/input Note: It is recommended to have a new Pyhton environment and then you can install the dependencies accordingly.
• Now follow these steps to install a local package(Custom BERTopic):

git clone https://github.com/atoti/BERTopic.git
cd ../gitclonedirectory/BERTopic
pip install -e .

Getting Started

For an in-depth overview of the features of BERTopic you can check the full documentation here or you can follow along with the Google Colab notebook here.

Quick Start

We start by extracting topics from the well-known 20 newsgroups dataset which is comprised of 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(
                       similarity_threshold_merging=0.5, # provide here you desired topics merging similarity threshold (between 0 and 1)
                       topic_words_diversity=0.5,        # provide here you desired keywords diversity ratio
                       stop_words = STOP_WORDS,          # STOP_WORDS is your custom stop words list
                       replace_dic = REPLACE,            # REPLACE is your custom dictionary of words replacement
              )
topics, _ = topic_model.fit_transform(docs)

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

>>> topic_model.get_topic_freq().head()
Topic	Count
-1	7288
49	3992
30	701
27	684
11	568

-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 49:

>>> topic_model.get_topic(49)
[('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)]

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

Visualize Topics

After having trained our BERTopic model, we can iteratively go through perhaps a hundred topic to get a good understanding of the topics that were extract. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis:

topic_model.visualize_topics()

Embedding Models

The parameter embedding_model takes in a string pointing to a sentence-transformers model, a SentenceTransformer, or a Flair DocumentEmbedding model.

Sentence-Transformers
You can select any model from sentence-transformers here and pass it through BERTopic with embedding_model:

from bertopic import BERTopic
topic_model = BERTopic(embedding_model="xlm-r-bert-base-nli-stsb-mean-tokens")

Or select a SentenceTransformer model with your own parameters:

from bertopic import BERTopic
from sentence_transformers import SentenceTransformer

sentence_model = SentenceTransformer("distilbert-base-nli-mean-tokens", device="cpu")
topic_model = BERTopic(embedding_model=sentence_model)

Flair
Flair allows you to choose almost any embedding model that is publicly available. Flair can be used as follows:

from bertopic import BERTopic
from flair.embeddings import TransformerDocumentEmbeddings

roberta = TransformerDocumentEmbeddings('roberta-base')
topic_model = BERTopic(embedding_model=roberta)

You can select any 🤗 transformers model here.

Custom Embeddings
You can also use previously generated embeddings by passing it through fit_transform():

topic_model = BERTopic()
topics, _ = topic_model.fit_transform(docs, embeddings)

Extract most relevant documents

You can extract the most relevant documents associated with any given topic as follows:

# Get the clusterer model, the clusters' tree and the clusters (topics ids)
clusterer = topic_model.hdbscan_model
tree = clusterer.condensed_tree_
clusters = tree._select_clusters()

# Get the ids of the most relevant documents (exemplars) associated with the topic at index idx
c_exemplars = topic_model.get_most_relevant_documents(clusters[idx], tree)

Overview

Methods	Code
Fit the model	topic_model.fit(docs])
Fit the model and predict documents	topic_model.fit_transform(docs])
Predict new documents	topic_model.transform([new_doc])
Access single topic	topic_model.get_topic(12)
Access all topics	topic_model.get_topics()
Get topic freq	topic_model.get_topic_freq()
Visualize Topics	topic_model.visualize_topics()
Visualize Topic Probability Distribution	topic_model.visualize_distribution(probabilities[0])
Update topic representation	topic_model.update_topics(docs, topics, n_gram_range=(1, 3))
Reduce nr of topics	topic_model.reduce_topics(docs, topics, nr_topics=30)
Find topics	topic_model.find_topics("vehicle")
Save model	topic_model.save("my_model")
Load model	BERTopic.load("my_model")
Get parameters	topic_model.get_params()

Citation

To cite BERTopic in your work, please use the following bibtex reference:

@misc{grootendorst2020bertopic,
  author       = {Maarten Grootendorst},
  title        = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.5.0},
  doi          = {10.5281/zenodo.4430182},
  url          = {https://doi.org/10.5281/zenodo.4430182}
}