Pattern Recognition
Plots combine different engineered features (Flesch, word count, lexical intensity). Bars often put unlike units beside each other for contrast—read labels, infer carefully.
Word length vs lexical intensity
Correlation analysis between post length and emotional intensity. Correlation coefficient: 0.51. Formula: r = Σ((x - x̄)(y - ȳ)) / √(Σ(x - x̄)² × Σ(y - ȳ)²)
Word Count vs Emotional Intensity
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Complexity vs Day of Week
Flesch reading ease score distribution by day of week. Higher scores indicate easier reading.
Complexity Score by Day of Week
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Weekday aggregates
Productivity and word count by day of week. Productivity = avg_word_count × post_count. Formula: productivity(day) = Σ(word_count for posts on day) × count(posts on day)
Day of Week Patterns
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Monthly aggregates
Creativity and complexity by month. Creativity = avg_word_count × avg_complexity. Formula: creativity(month) = mean(word_count for posts in month) × mean(complexity for posts in month)
Seasonal Patterns
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Post-index modulo “phases” (toy)
Labels come from postIndex % 4—good for demos, weak causal story.
Creative Cycle Phases
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Argmax snapshots (engineering composites)
“Productivity” = mean words × post count for that weekday; “creativity” = mean words × mean Flesch for that calendar month—these mixes are deliberate heuristics, not latent traits.
| Peak weekday composite | 23777 | Friday |
| Peak month composite | 96669 | February |
| Peak cycle avg words | 1593 | Creation |
Statistical Pattern Analysis
Identified patterns based on quantitative analysis. Confidence levels determined by statistical significance of observed patterns.
Day of Week Metrics
Quantitative metrics by day of week. Productivity = avg_word_count × post_count. Grades calculated as percentile of value relative to maximum.
Seasonal Metrics
Quantitative metrics by month of year. Creativity = avg_word_count × avg_complexity. Grades calculated as percentile of value relative to maximum.
Creative Cycle Phase Metrics
Metrics by creative cycle phase. Phases assigned sequentially: cycle_phase = post_index % 4. Grades calculated as percentile relative to maximum.
Tag Co-occurrence Analysis
Tag pairs that appear together frequently. Average word count calculated for posts containing both tags. Formula: avg_word_count(combo) = Σ(word_count for posts with combo) / count(posts with combo)
Posts with Highest Word Count
Posts ranked by word count. Pattern classification: Long-form (>1000 words) vs Short-form, Emotional (intensity >5) vs Analytical, Complex (Flesch >50) vs Simple.
Narrative summary
Analysis identifies 1 significant challenges. 5 objective metrics provide baseline data.