Blog Voice Analyzer

Blog Voice Analyzer

Analyze blog articles for AI-sounding patterns using NLP, then use the diagnostics to improve the writing before publishing. The goal is content that reads more human than virtually all other blog articles — and avoids being flagged by Google's helpful content signals.

Invoked by the user with /blog-voice-analyzer or when asked to "check" or "analyze" a blog post for AI patterns.

Quick Start

pipenv run python3 scripts/blog/ai_voice_analyzer.py <path-to-markdown-file>

The script accepts any text file (markdown, plain text). It strips frontmatter, HTML, and markdown formatting before analysis.

What It Detects

The analyzer runs 18 independent checks organized into four categories. Each scores 0-100 (higher = more human), and the overall score is a weighted average.

Structural Patterns

Vocabulary & Word Choice

Voice & Personality

Micro Patterns

How to Read the Output

Overall Score

Flagged Sentences

Only sentences with 2+ issues are flagged (reduces noise). Each is labeled HIGH/MED severity with specific diagnostics like:

[HIGH] #13:
"The variability in charging station availability, especially in rural areas, poses a challenge..."
  → Vague verb: "poses a"
  → Nominalization-heavy (4): variability, station, availability, distance
  → Length (20w) ≈ average (21w)

These are the sentences to rewrite first.

Priority Fixes

The summary lists the 5 worst-scoring dimensions with specific actions. Address these in order.

How to Use the Output to Improve Content

Step 1: Run the Analyzer

pipenv run python3 scripts/blog/ai_voice_analyzer.py path/to/article.md

Step 2: Fix Priority Items Top-Down

Work through the priority fixes list. The most impactful improvements by category:

If Personality scores low (< 70):

• Add contractions throughout: "do not" → "don't", "it is" → "it's", "you are" → "you're"
• Add first person for opinions: "I find...", "In my experience...", "We've all been there"
• Add second person to engage the reader: "You know that feeling when..."

If Questions score low (< 50):

• Turn topic sentences into questions: "Decision fatigue depletes willpower" → "Why does making too many choices drain you?"
• Add rhetorical questions that make the reader feel seen: "Ever notice how the simplest decisions feel impossible by 8pm?"
• End sections with a question that leads to the next point

If Readability scores low (< 70):

• Replace latinate words with shorter ones: "utilize" → "use", "approximately" → "about", "demonstrate" → "show"
• Break long sentences at semicolons and em dashes
• Read it out loud — if you wouldn't say it to a friend, simplify it

If Nominalization scores low (< 70):

• Find every -tion/-ment/-ness noun and convert to active verb:
  • "The reduction of stress" → "Reducing stress"
  • "The implementation of the system" → "Implementing the system"
  • "His contribution to the project" → "He contributed to the project"

If Hedge Phrases are detected:

• Delete them. Every single one. They add no meaning:
  • "It's important to note that X" → "X"
  • "In today's fast-paced world" → delete entirely
  • "multifaceted" → "complex" or just describe the specific facets

If Vague Verbs are detected:

• Replace with direct assertions:
  • "X contributes to Y" → "X causes Y" or "X increases Y"
  • "X remains a significant challenge" → "X is still hard" or explain specifically why
  • "X aims to achieve Y" → "X does Y" or "X tries to Y"

If Sentence Variance is low (< 70):

• Add 3-5 word fragment sentences: "That's the point." / "And it works." / "Here's why."
• Break one paragraph's longest sentence into two
• Start a sentence with "But" or "And"

Step 3: Re-run and Compare

pipenv run python3 scripts/blog/ai_voice_analyzer.py path/to/article-v2.md

Target: overall score > 80, zero HIGH-severity flagged sentences.

Step 4 (Optional): Run the Model Benchmark

To compare how different AI models perform on similar prompts:

pipenv run python3 scripts/blog/benchmark_models.py

This generates 3 articles per model (Claude Sonnet, Haiku; GPT-4o, 4o-mini, o3-mini) on psychology/productivity topics and scores them all. Edit TOPICS and MODELS in the script to customize.

Known Limitations

Things the analyzer catches well:

• Vocabulary patterns (hedge phrases, signal words, nominalizations, vague verbs)
• Structural monotony (sentence length, opener repetition, clause depth)
• Voice absence (no personality, no questions, no contractions)
• Register mismatch (academic grade level for blog content)

Things the analyzer does NOT catch:

• Semantic emptiness — a sentence can sound varied and use no hedge phrases but still say nothing substantive. The tool can't judge whether content is insightful vs. generic platitudes.
• Template structure at the section level — Topic → Explanation → Topic → Explanation repeating throughout a piece is a strong AI pattern, but the tool only looks at sentence and paragraph level, not section-level templates.
• Cliche ideas — the tool checks how you write, not what you write. An article full of obvious advice ("get enough sleep", "exercise regularly") will score fine structurally.
• Over-hedging without trigger phrases — a passage can be wishy-washy and non-committal without using any of the specific phrases in our list.
• Images, formatting, and layout — the tool only analyzes prose text.
• Short text — articles under ~300 words produce unreliable scores because statistical measures need volume. Some dimensions (TTR, sentence variance) are especially noisy on short texts.
• Topic word inflation — the word repetition detector tries to exclude topic words (extracted from the title and first paragraph), but it's imperfect. If your article's subject uses common words, they may still get flagged.
• Style vs. defect — some writers deliberately use simple vocabulary (low TTR), avoid questions (declarative style), or write uniformly-sized paragraphs. The tool treats these as weaknesses because they correlate with AI output, but they may be intentional style choices. Use judgment.
• False paired adjective flags — some paired adjectives are natural and effective ("fast and powerful"). The tool applies a per-1000-words density threshold but can still flag good writing.

Benchmark Reference Scores

From testing across known human and AI text:

Target for published content: 80+ with zero HIGH-severity flagged sentences.

Technical Details

Dependencies: spacy, textstat (Python, installed via pipenv). Requires the en_core_web_sm spaCy model.

How scoring works: Each of the 18 dimensions scores 0-100. The overall score is a weighted average with personality (0.12), hedge phrases (0.10), entity density (0.08), sentence variance (0.08), and vague verbs (0.07) weighted highest. Full weight table is in scripts/blog/ai_voice_analyzer.py in the analyze() function.

Sentence flagging threshold: Only sentences with 2+ co-occurring issues are flagged. Single-issue sentences are not surfaced to reduce noise. The "length close to average" flag only triggers when the document's overall sentence length std dev is below 6 (indicating genuine monotony).

Files