Practical: Clustering of page content with LDA – Lesson Preview
Topic modeling helps you make sense of large content libraries without manual labels. In this practical lesson, you’ll learn how to group website pages by underlying themes using LDA (Latent Dirichlet Allocation); first with a no-code web app, then with a reusable Python notebook in Google Colab. You’ll see how LDA uncovers hidden topics by analyzing how words co-occur across documents, then outputs topic keywords, document-to-topic probabilities, and topic-to-topic correlations you can put to work.
For Marketing and SEO professionals, this matters now because content ecosystems are big and messy. LDA gives you an interpretable map of themes across your site, competitors, or even YouTube/Reddit transcripts. The lesson compares LDA’s strengths and limits, shows what “good” looks like via coherence/perplexity, and walks through hyperparameter tuning (including alpha/beta) to improve quality. You’ll also see how to turn outputs into actions, like internal linking modules and faster competitor/market audits.
What you’ll learn (why it matters)
- Run LDA no-code and in Python — because repeatable workflows scale topic analysis.
- Interpret topic outputs — because keywords, probabilities, and correlations guide decisions.
- Tune hyperparameters (alpha, beta) — because better coherence improves topic quality.
- Validate models with coherence/perplexity — because metrics prevent misleading clusters.
- Apply insights to SEO tasks — because internal linking and audits need data, not guesses.
Key concepts (with mini-definitions)
- LDA (Latent Dirichlet Allocation) — a generative Bayesian model that infers hidden topics from word co-occurrence.
- Corpus — the collection of documents analyzed (e.g., pages, transcripts).
- Topic — a probability-weighted group of words that often appear together.
- Document–topic distribution — probabilities showing how much each topic appears in a page.
- Soft/fuzzy clustering — documents can belong to multiple topics with varying probabilities.
- Perplexity — how well the model predicts unseen text (lower is better).
- Coherence score — how semantically consistent topic words are (higher is better).
- Hyperparameters (alpha, beta) — settings shaping document–topic and topic–word distributions.
Tools mentioned
Cornell no-code LDA web app, Google Colab, pyLDAVis, word cloud, Google Sheets template, stopwords list and Google Natural Language (referenced dataset)
Practice & readings
- Run the Cornell web app: upload documents + stopwords, set topics/iterations, download outputs.
- Use the provided Google Colab: preprocess text, train LDA, visualize with pyLDAVis, compute coherence/perplexity, tune alpha/beta.
- Organize outputs with the Google Sheets template to spot topic correlations and dominant topics per page.
Key insights & takeaways
- LDA is an older but foundational topic model that remains useful and interpretable.
- Good topic models require iteration and tuning; they are exploratory, not one-and-done.
- Topic probabilities and correlations directly inform internal linking and content organization.
- The same workflow applies to competitors and market sources (e.g., YouTube/Reddit).
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