
Leveraging BERT and c-TF-IDF to create easily interpretable topics.

BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters, allowing for easily interpretable topics while preserving important words in the topic descriptions. It supports various topic modeling techniques including guided, supervised, semi-supervised, manual, hierarchical, class-based, dynamic, online/incremental, multimodal, multi-aspect, text generation/LLM, zero-shot, merge models, and seed words. BERTopic is modular, allowing users to swap out components like embedding models (sentence-transformers, Flair, Gensim, spaCy, USE), dimensionality reduction (UMAP), and clustering algorithms (HDBSCAN). It also provides visualizations similar to LDAvis and allows fine-tuning topic representations using methods like KeyBERTInspired and OpenAI models (ChatGPT) for generating labels, summaries, and keywords.
BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters, allowing for easily interpretable topics while preserving important words in the topic descriptions.
Explore all tools that specialize in topic visualization. This domain focus ensures BERTopic delivers optimized results for this specific requirement.
Allows users to swap out the default sentence-transformers with models like Flair, Gensim, spaCy, or USE.
Enables modeling different aspects of topics simultaneously, providing a more comprehensive view.
Models topics over time, capturing the evolution of themes in a corpus.
Leverages models like ChatGPT for topic representation, label generation, and summarization.
Organizes topics in a hierarchical structure, providing different levels of granularity.
Offers a light-weight installation without transformers, UMAP, and HDBSCAN for Model2Vec or inference.
Install BERTopic: `pip install bertopic`
Import BERTopic: `from bertopic import BERTopic`
Load your documents: `from sklearn.datasets import fetch_20newsgroups; docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']`
Initialize the model: `topic_model = BERTopic()`
Fit and transform the documents: `topics, probs = topic_model.fit_transform(docs)`
Access topic information: `topic_model.get_topic_info()`
Visualize topics: `topic_model.visualize_topics()`
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"Users praise its interpretability and flexibility but note its sensitivity to parameter tuning."
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