DiscorraDiscorra

Understanding the Dashboard & Tabs

A guided tour of Discorra’s analysis dashboard and the seven core tabs — what each view shows, when to use it, and what to do next.

Updated 9/12/2025

Understanding the Dashboard & Tabs

Discorra’s dashboard organizes your analysis into seven tabs so you can move from patterns → implications → actions.
This article explains each tab, what you’ll see, and how to use the results.

Overview

Overview dashboard screenshot

Purpose: A high-level scan to orient stakeholders before deep dives.

What you’ll see

  • Frequency highlights and keyness (distinctive terms via z-scores).
  • Topic mix and micro-charts for quick volume vs. distinctiveness.
  • A compact resonance snapshot for message overlap/divergence.

Use it to

  • Identify promising themes to explore next.
  • Triage which tabs merit a deeper pass.

Concordance

Concordance KWIC screenshot

Purpose: Anchor charts back to evidence with KWIC (Key Word In Context).

What you’ll see

  • In-context lines for any focus term.
  • Neighbor words that frame meaning and tone.
  • Export options for audit, markup, or legal review.

Use it to

  • Validate polarity or framing before recommending changes.
  • Pull verbatim examples into stakeholder decks.

Collocates

Collocates analysis screenshot

Purpose: Surface frames, claims, objections, and risky pairings around a focus word.

What you’ll see

  • Windowed co-occurrence with strength scores and noise cutoffs.
  • Side-by-side by corpus to reveal what’s native vs. borrowed.
  • Adjustable window size to match your brief.

Use it to

  • Spot message bundles worth amplifying (or avoiding).
  • Design tests around the strongest supportive phrases.

Resonance

Resonance scoring screenshot

Purpose: Measure overlap vs. divergence in vocabulary strength between corpora.

What you’ll see

  • A 0–1 resonance score derived from normalized frequencies and z-scores.
  • Lists of shared and distinctive terms/phrases.
  • Controls to dial overlap and avoid “me-too” phrasing.

Use it to

  • Quantify alignment with customers or competitors.
  • Identify whitespace where your language can differentiate.

Sentiment

Sentiment compare screenshot

Purpose: Compare tone across corpora.

What you’ll see

  • Positive / Neutral / Negative distributions per corpus.
  • Δ Positivity and a normalized comparative score.
  • Side-by-side bars plus click-through to KWIC for validation.

Use it to

  • Benchmark brand vs. audience tone.
  • Track shifts in positivity or risk over time.

Messaging

Messaging builder screenshot

Purpose: Turn findings into testable statements and pillar maps.

What you’ll see

  • Pillars (big nodes), supporting phrases (small nodes), and gap halos.
  • Pillar cards with salience %, keywords, and optional sentiment overlays.
  • Drafting tools to assemble candidate lines with provenance links.

Use it to

  • Build a messaging plan grounded in evidence.
  • Export variants for A/B testing and creative reviews.

Readout

Stakeholder readout screenshot

Purpose: A concise, shareable narrative: problem → pattern → implication → recommendation.

What you’ll see

  • Auto-assembled sections using selected charts and captions.
  • Inline links back to KWIC and source panels.
  • One-click export for stakeholder share-outs.

Use it to

  • Socialize insights quickly with clear provenance.
  • Keep teams aligned on decisions and next steps.

Tips for workflow

  1. Start broad → go narrow: Overview ➜ Resonance/Sentiment ➜ Concordance/Collocates ➜ Messaging ➜ Readout.
  2. Cross-validate: Don’t rely on a single metric; confirm a pattern appears in at least two views.
  3. Cite evidence: Pull KWIC lines into Messaging and Readout to maintain traceability.

Further Reading

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