Best Practices for Comparing Corpora

Comparing corpora is the foundation of analysis in Discorra.
Done well, it reveals shared language, distinct signals, and actionable insights.
Done poorly, it can produce misleading results.

This article outlines best practices to help you get the most value from your comparisons.

Choose the right corpora

Align to your question: Select datasets that truly represent the perspectives you want to compare (e.g., brand voice vs. customer reviews).
Avoid noise: Clean or filter irrelevant content before running analyses.
Be consistent: Use comparable text types (e.g., social posts vs. social posts) to minimize structural bias.

Balance size and scope

Size matters: Extremely small corpora may not yield reliable comparisons.
Scope matters more: Make sure datasets cover similar domains — avoid comparing short marketing blurbs to long-form academic papers.
Tip: Aim for corpora with at least several thousand tokens each for robust results.

Normalize your data

Frequency normalization: Always compare terms per 10k tokens, not raw counts.
Stopword filtering: Remove common function words that don’t carry meaning.
Consistent preprocessing: Apply the same tokenization, lemmatization, and language settings to both datasets.

Use multiple measures

Discorra provides complementary views of comparison. Combine them for deeper insights:

Frequency & Keyness: What is most common vs. most distinctive.
Resonance: Where corpora overlap or diverge in vocabulary strength.
Sentiment: Differences in tone, not just terms.
Messaging: How language groups into pillars and gaps.

Interpret overlap vs. divergence

High overlap: Suggests shared voice or alignment (e.g., brand successfully echoing customer concerns).
Low overlap: Signals differentiation or misalignment, depending on context.
Gaps: Pay attention to what is missing in one corpus — this often reveals white space or blind spots.

⚠️ Caution: High similarity does not always mean success, and divergence is not always bad. The value depends on your strategic goal (alignment vs. differentiation).

Validate and iterate

Cross-check: Use multiple analyses to confirm findings (e.g., a resonance gap that also shows up in messaging).
Contextualize: Interpret results within industry, culture, or channel norms.
Iterate: Refine corpora selection, filters, or timeframes to sharpen insights.

Why best practices matter

Following these principles ensures your comparisons are:

Reliable — grounded in balanced, normalized data
Meaningful — aligned with strategic questions
Actionable — producing insights you can confidently use in messaging, strategy, and research