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A Practical Guide to BERTopic for Transformer-Based Topic Modeling
- BERTopic is a python library for transformer-based topic modeling, useful in various NLP applications like document tagging and content organization.
- BERTopic consists of 6 core modules for topic modeling customization: Embeddings, Dimensionality Reduction, Clustering, Vectorizers, c-TF-IDF, and Representation Model.
- Using sentence-transformer models, BERTopic converts text into semantic embeddings, with options like 'all-MiniLM-L6-v2' and 'BAAI/bge-base-en-v1.5'.
- Dimensionality Reduction techniques like UMAP are vital for reducing high-dimensional embeddings to improve cluster formation.
- Clustering involves grouping text documents into topics using models like HDBSCAN and K-Means based on semantic similarity.
- Vectorizer options like CountVectorizer help create matrix representations of terms in documents to improve topic analysis.
- c-TF-IDF focuses on reducing frequently encountered words across clusters by evaluating keyword importance at the cluster level.
- Representation Model leverages semantic similarity to refine topic keywords, offering options like KeyBERTInspired for better topic descriptions.
- Practical application on Apple financial news data demonstrates the effectiveness of BERTopic modules in identifying meaningful topics.
- Experimentation and customization of each BERTopic module help improve topic representations and reveal insights from textual data.
- BERTopic's versatility and customizable modules make it a powerful tool for transformer-based topic modeling in NLP tasks.
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