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About Messaging Analysis

An overview of Messaging Analysis in Discorra — how pillars are defined, how gaps are visualized, and how to interpret messaging alignment between corpora.

Updated 9/12/2025

About Messaging Analysis

Messaging Analysis helps you understand how ideas are structured, grouped, and aligned across corpora.
It identifies pillars (core themes), phrases that support those pillars, and gaps where one dataset is silent compared to the other.



What is messaging analysis?

Messaging analysis goes beyond frequency counts and looks at semantic structure.
Instead of just asking what words are shared?, it asks what stories are being told, and where do they overlap or diverge?

Definition
Messaging analysis groups semantically related key phrases into pillars, compares coverage between corpora, and highlights gaps.


How pillars are identified

Discorra builds pillars through three stages:

  1. Semantic grouping
    Related key phrases are clustered into themes (pillars).

  2. Lexicon assignment
    Each pillar is tagged with representative terms and phrases.

  3. Sentiment overlay
    A lexicon flags sentences for pillar-level sentiment, giving insight into tone as well as content.


How gaps are revealed

  • Overlap (green): Both corpora use the same language under a pillar.
  • Gap (amber): One corpus is active in a pillar while the other is silent.
  • Salience %: Indicates how central a pillar is in shaping the corpus.

The network graph shows pillars as big nodes, phrases as smaller nodes, and gaps as amber halos.
This makes it easy to spot not just what is said, but what is missing.


How to interpret the results

  • Strong overlap: Suggests both corpora are aligned on the same messaging themes.
  • Distinct pillars: Shows where each corpus emphasizes different narratives.
  • Gap halos: Mark strategic opportunities — either a risk (competitors speaking where you are not) or whitespace (room to differentiate).

⚠️ Note: Messaging analysis highlights what is said and how it is structured. It does not assess statistical keyness (see About Frequency & Keyness).


Why messaging matters

Messaging analysis helps you:

  • Detect pillar misalignment between brand and audience
  • Identify whitespace opportunities for differentiation
  • Track shifts in discourse structure across time or channels
  • Evaluate campaign consistency against strategic themes

Example:
A company comparing Jazz vs. Blues corpora may see strong overlap on “music” language, but a gap on “artistic heritage.” That suggests a potential storytelling lever.


Next steps


Further Reading