Topic Modeling with BERT

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Key steps in BERTopic modelling are as following.

• Use “Sentence Embedding” models to embed the sentences of the article
• Reduce the dimensionality of embedding using UMAP
• Cluster these documents (reduced dimensions) using HDBSAN
• Use c-TF-IDF extract keywords, their frequency and IDF for each cluster.
• MMR: Maximize Candidate Relevance. How many words in a topic can represent the topic?
• Intertopic Distance Map
• Use similarity matrix (heatmap), dandogram (hierarchical map), to visualize the topics and key_words.
• Traction of topic over time period. Some may be irrelevant and for other traction may be increasing or decreasing.

Installation

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

pip install bertopic

# If you want to install BERTopic with other embedding models, you can choose one of the following:

# Choose an embedding backend
pip install bertopic[flair, gensim, spacy, use]

# Topic modeling with images
pip install bertopic[vision]

Supported Topic Modelling Techniques

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BERTopic supports all kinds of topic modeling techniques as below.

• Guided
• Supervised
• Semi-supervised
• Manual
• Multi-topic distributions
• Hierarchical
• Class-based
• Dynamic
• Online/Incremental
• Multimodal
• Multi-aspect
• Text Generation/LLM
• Merge Models

Related Resources

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• Advanced Topic Modeling with BERTopic by PINECONE
• BERTopic by SpaCy
• BERTopic github
• BERTopic by Huggingface

Tools in BERTopic

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Best Topic Modeling Tool in BERTopic

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BERTopic Model Building

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Application

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• arXiv Dataset (1.7m+ STEP papers)
• Images/photographs
• Historical Documents
• News articles