Practical: Keyword Clustering with KeyBERT – Lesson Preview
In this hands-on lesson, you’ll learn how to use KeyBERT, a natural language processing model built on Google’s BERT, to cluster keywords based on their semantic similarity. This practical tutorial bridges theory and application, showing you how to automatically group keywords into meaningful clusters for SEO and content strategy.
You’ll see how KeyBERT extracts contextually relevant keywords from text, generates unigram and bigram clusters, and produces exportable results you can immediately use for query analysis, topical mapping, and content briefs. The lesson also demonstrates how to pair these clusters with SEO metrics like search volume, keyword difficulty and SERP features and visualize the results in Looker Studio for deeper insights.
If you’ve ever struggled with manual keyword grouping or messy spreadsheets, this workflow will help you automate the process in minutes while maintaining accuracy and context.
What you’ll learn (why it matters)
- Cluster keywords semantically — because context-aware grouping surfaces more relevant topics.
- Use KeyBERT for keyword extraction — because it leverages BERT embeddings for deeper meaning.
- Automate keyword clustering — because it saves hours of manual tagging work.
- Visualize clusters with Looker Studio — because insights are clearer when seen in context.
- Combine clusters with SEO metrics — because it helps prioritize and refine strategy.
Key concepts (with mini-definitions)
- KeyBERT — a keyword extraction model using BERT to identify contextually relevant terms.
- BERT (Bidirectional Encoder Representations from Transformers) — Google’s NLP model that understands language context in both directions.
- Unigram — a single-word keyword extracted as the core topic.
- Bigram — a two-word phrase representing a richer semantic cluster.
- Embeddings — numerical representations of words that capture meaning and relationships.
- Cosine similarity — the metric KeyBERT uses to compare similarity between text embeddings.
- n-gram extraction — customization that allows single-, double-, or multi-word phrase clustering.
- Semantic clustering — grouping data based on contextual similarity, not just word matching.
Tools mentioned
KeyBERT, BERT, scikit-learn, Sentence Transformers and Looker Studio.
Practice & readings
- Upload and cluster your own keyword CSV or Excel file using KeyBERT.
- Explore the provided Looker Studio dashboard to visualize keyword clusters and metrics.
- Optional: Test unigram vs. bigram outputs to see how context changes.
Key insights & takeaways
- KeyBERT uses contextual embeddings, not just word frequency, for smarter keyword grouping.
- Clustering 1,000+ keywords can be done in minutes with minimal setup.
- Pairing clusters with SEO metrics creates actionable, data-rich insights.
- Visualization amplifies understanding, making complex data instantly usable.
- Semantic-based clustering yields more accurate results than fuzzy or distance-based methods.
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