big-five-personality

name	big-five-personality
description	Use when inferring Big Five (OCEAN) personality traits from a text corpus, mapping linguistic markers to personality dimensions, converting trait profiles into writing style constraints for voice replication, or needing to characterize an author's personality tendencies from their writing patterns

Big Five (OCEAN) Personality Profiling

Overview

Infer Big Five personality trait positions from linguistic markers in a text corpus, then map each trait to concrete writing style constraints for voice replication. The core principle: personality influences word choice in measurable but modest ways -- the mean absolute correlation between Big Five traits and LIWC categories is ~0.14 (Yarkoni, 2010), with the strongest single correlation at ~0.23. This means text-based personality inference reveals tendencies, never diagnoses. Treat all outputs as relative positions on a continuum with wide confidence bands, not as fixed personality scores.

Research foundation: The 2022 meta-analysis by Stachl et al. in Psychological Bulletin (N = 85,724 across 26 studies) established that 20 linguistic categories constitute a "kernel of truth" correlating with both self- and observer-reported Big Five traits. Self-report correlations are small (|r| = .08-.14); observer-report correlations are small-to-medium (|r| = .18-.39). The 52 LIWC categories collectively explain ~5.1% of self-reported personality variance, but ~38.5% of observer-reported variance.

When to Use

• Characterizing an author's personality tendencies from a corpus of their writing
• Building a personality profile to inform writing style replication or voice matching
• Quantifying linguistic markers associated with each OCEAN dimension
• Comparing a writing sample's trait indicators against population baselines
• Feeding trait positions into archetype classification or persona construction

When NOT to use:

• Diagnosing personality disorders or clinical conditions (this is NOT a clinical tool)
• Making hiring, screening, or gatekeeping decisions based on inferred traits
• Corpus contains fewer than 1,000 words (see Insufficient Data Handling)
• Text is translated, heavily edited by others, or ghostwritten (author signal is diluted)
• Single-context writing only (e.g., all work emails) -- context constrains language and masks personality

digraph personality_decision {
    rankdir=TB;
    "Enough text?\n(1000+ words)" [shape=diamond];
    "Multiple contexts\nor topics?" [shape=diamond];
    "Clinical/hiring\npurpose?" [shape=diamond];
    "Proceed with\nfull analysis" [shape=box];
    "Report insufficient\ndata" [shape=box];
    "Proceed with\ncontext caveat" [shape=box];
    "STOP: Not appropriate\nfor this use case" [shape=box];

    "Enough text?\n(1000+ words)" -> "Multiple contexts\nor topics?" [label="yes"];
    "Enough text?\n(1000+ words)" -> "Report insufficient\ndata" [label="no"];
    "Multiple contexts\nor topics?" -> "Clinical/hiring\npurpose?" [label="yes"];
    "Multiple contexts\nor topics?" -> "Proceed with\ncontext caveat" [label="no, single context"];
    "Clinical/hiring\npurpose?" -> "STOP: Not appropriate\nfor this use case" [label="yes"];
    "Clinical/hiring\npurpose?" -> "Proceed with\nfull analysis" [label="no"];
}

Quick Reference

The Five Traits and Their Linguistic Markers

Trait	High-Trait Linguistic Indicators	Low-Trait Linguistic Indicators
Openness (O)	Articles, prepositions, long words (6+ chars), insight words ("think", "know", "consider"), tentative language, abstract/intellectual vocabulary, cultural references, varied vocabulary (high type-token ratio)	Short words, concrete/simple vocabulary, low type-token ratio, few insight words, past-tense orientation
Conscientiousness (C)	Achievement words ("earn", "win", "completed"), exclusion words ("but", "except", "without"), negations used for precision, formal structure, longer sentences, organized discourse markers	Fewer achievement words, more filler words, shorter sentences, informal structure, present-tense hedging
Extraversion (E)	Social words ("friend", "talk", "party"), positive emotion words, 1st person plural ("we", "us"), references to people/groups, exclamation marks, informal/conversational tone, shorter words	Fewer social references, more articles/prepositions (task-oriented), longer words, formal tone, 1st person singular, fewer exclamations
Agreeableness (A)	Positive emotion words, affirmation ("agree", "wonderful", "great"), 1st person singular (expressing empathy), words about family/home/friends, fewer swear words, hedging/softening language	Negative emotion words, swear words, words about money/death, direct/blunt phrasing, fewer affirmations, competitive language
Neuroticism (N)	1st person singular ("I", "me", "my"), negative emotion words ("awful", "worried", "terrible"), anxiety words, sadness words, anger words, more past tense, hedging/uncertainty markers	Fewer 1st person singular, fewer negative emotion words, more present tense, confident/certain language, fewer anxiety/worry words

Key Metric Thresholds

Parameter	Value	Source
Minimum corpus size (basic)	1,000 words	IBM Watson Personality Insights research
Minimum corpus size (reliable)	3,000+ words	IBM Watson validation studies
Optimal corpus size	5,000-10,000+ words	Yarkoni (2010): bloggers averaged 115,000 words
Expected correlation strength	r = 0.08-0.23	Stachl et al. (2022) meta-analysis
Variance explained (self-report)	~5.1%	52 LIWC categories combined
Variance explained (observer)	~38.5%	52 LIWC categories combined
Population baseline	Score 50 on 0-100 scale	By definition, population mean is the midpoint
Confidence band (typical)	+/- 15-20 points	On a 0-100 normalized scale

Workflow

Copy this checklist and track progress:

Big Five Personality Profiling Progress:
- [ ] Step 1: Validate corpus suitability and size
- [ ] Step 2: Compute linguistic marker frequencies per trait
- [ ] Step 3: Score each trait dimension (raw indicators)
- [ ] Step 4: Normalize scores and compute confidence bands
- [ ] Step 5: Map trait positions to writing style constraints
- [ ] Step 6: Generate the personality-informed writing profile
- [ ] Step 7: Write findings to docs/analysis/15-big-five-personality.md

Step 1: Validate Corpus Suitability

Before analysis, verify the corpus can support personality inference.

Suitability checks:

Check	Pass Condition	Fail Action
Word count	1,000+ words total	Below 1,000: STOP. Report insufficient data. Do not compute trait scores.
Word count confidence	3,000+ words for moderate confidence	1,000-3,000: Proceed but flag ALL scores as "low confidence."
Language	Predominantly English	Non-English text invalidates LIWC-based marker lists. Flag or exclude.
Authorship	Single author or known author set	Mixed/unknown authorship dilutes signal. Document limitation.
Context diversity	Writing spans 2+ topics or contexts	Single-context writing (all work emails, all game reviews) constrains language. Trait scores reflect the context, not the person. Flag prominently.
Editing level	Minimally edited, natural writing	Heavily edited/ghostwritten text reflects editorial voice, not author personality.
Temporal span	Multiple writing occasions preferred	A single long document from one sitting may reflect mood, not trait.

If corpus fails suitability: Report the failure. State what was checked, what failed, and why personality inference is unreliable for this corpus. Do not force analysis on unsuitable data.

Step 2: Compute Linguistic Marker Frequencies

For each trait dimension, count occurrences of validated linguistic markers across the corpus.

Marker categories to measure (per LIWC-validated research):

# Validated marker categories derived from Pennebaker & King (1999),
# Yarkoni (2010), and Stachl et al. (2022) meta-analysis.
# These are CATEGORY-LEVEL indicators, not exhaustive word lists.

TRAIT_MARKERS = {
    'openness': {
        'positive_indicators': [
            'articles (a, an, the)',           # r ~ .10-.15
            'prepositions',                     # r ~ .08-.12
            'long words (6+ characters)',       # vocabulary complexity
            'insight words (think, know, consider, understand)',
            'tentative words (maybe, perhaps, guess)',
            'high type-token ratio',            # vocabulary diversity
            'abstract nouns',
            'cultural/intellectual references',
        ],
        'negative_indicators': [
            'present-tense verbs (overuse)',
            'concrete/simple vocabulary',
            'low type-token ratio',
            'home/family words (inversely correlated)',
        ],
    },
    'conscientiousness': {
        'positive_indicators': [
            'achievement words (earn, win, complete, success)',
            'exclusion words (but, except, without)',  # precision
            'negations used for precision',
            'longer average sentence length',
            'discourse markers (therefore, however, furthermore)',
            'future-tense verbs',
            'organized paragraph structure',
        ],
        'negative_indicators': [
            'filler words (um, like, basically)',
            'informal abbreviations',
            'very short sentences (fragmented)',
            'present-tense hedging',
        ],
    },
    'extraversion': {
        'positive_indicators': [
            'social words (friend, talk, share, together)',
            'positive emotion words (happy, great, love, wonderful)',
            '1st person plural (we, us, our)',
            'references to people and groups',
            'exclamation marks',
            'informal/conversational register',
            'shorter average word length',
        ],
        'negative_indicators': [
            'articles (task-focused writing)',   # inversely correlated
            'long words',
            '1st person singular (I-focused)',
            'formal register',
            'prepositions (complex structure)',
        ],
    },
    'agreeableness': {
        'positive_indicators': [
            'positive emotion words',
            'affirmation/agreement words (agree, wonderful, thank)',
            'family words (home, mother, friend)',
            'social process words',
            'hedging/softening (perhaps, might, I think)',
            'compliments and praise',
            'inclusive language (we, together)',
        ],
        'negative_indicators': [
            'swear words',
            'anger words',
            'death/violence references',
            'money/power words',
            'direct commands/imperatives',
            'competitive language',
        ],
    },
    'neuroticism': {
        'positive_indicators': [  # HIGH neuroticism markers
            '1st person singular (I, me, my, mine)',  # strongest marker
            'negative emotion words (awful, terrible, worried)',
            'anxiety words (nervous, afraid, tense)',
            'sadness words (cry, grief, sad)',
            'anger words (hate, annoyed, furious)',
            'past-tense verbs',
            'certainty-reducing hedges (I guess, maybe, not sure)',
        ],
        'negative_indicators': [  # LOW neuroticism (emotional stability)
            'positive emotion words',
            'present-tense verbs',
            'certainty words (always, definitely, clearly)',
            'fewer self-references',
        ],
    },
}

Measurement approach:

1. Tokenize the corpus (preserve case for emphasis detection, then lowercase for counting)
2. Count words falling into each marker category using a validated word list (LIWC-22 if available, or open alternatives like VADER lexicon for sentiment categories, NRC EmoLex for emotion categories)
3. Compute the frequency rate per marker category: count / total_words * 100
4. Track structural markers separately: average sentence length, type-token ratio, punctuation patterns, pronoun ratios

import re
from collections import Counter

def compute_basic_markers(text):
    """Compute foundational linguistic markers for personality inference.
    This provides the raw measurements; trait scoring is in Step 3."""
    words = text.lower().split()
    total_words = len(words)
    if total_words == 0:
        return None

    unique_words = set(words)
    sentences = re.split(r'[.!?]+', text)
    sentences = [s.strip() for s in sentences if s.strip()]

    # Pronoun categories
    i_pronouns = {'i', 'me', 'my', 'mine', 'myself'}
    we_pronouns = {'we', 'us', 'our', 'ours', 'ourselves'}

    # Word length distribution
    long_words = [w for w in words if len(w) >= 6]

    markers = {
        'total_words': total_words,
        'unique_words': len(unique_words),
        'type_token_ratio': len(unique_words) / total_words,
        'avg_word_length': sum(len(w) for w in words) / total_words,
        'avg_sentence_length': total_words / max(len(sentences), 1),
        'long_word_pct': len(long_words) / total_words * 100,
        'i_pronoun_pct': sum(1 for w in words if w in i_pronouns) / total_words * 100,
        'we_pronoun_pct': sum(1 for w in words if w in we_pronouns) / total_words * 100,
        'exclamation_rate': text.count('!') / max(len(sentences), 1),
        'question_rate': text.count('?') / max(len(sentences), 1),
    }
    return markers

Step 3: Score Each Trait Dimension

Convert raw marker frequencies into relative trait positions. Each trait is scored on a continuum, NOT as a binary classification.

Scoring approach:

For each trait, compute a weighted composite from its marker categories. The weights reflect the meta-analytic correlation strengths from Stachl et al. (2022).

Scoring heuristic (when validated tools like LIWC-22 are unavailable):

For each trait dimension:

1. Count the frequency of positive indicators (markers associated with HIGH trait levels)
2. Count the frequency of negative indicators (markers associated with LOW trait levels)
3. Compute a directional score: (positive_rate - negative_rate) / (positive_rate + negative_rate)
4. This yields a raw score from -1.0 (strongly low-trait) to +1.0 (strongly high-trait)
5. Center of 0.0 = indeterminate / population average

Trait scoring table:

Trait	Primary Markers (highest r)	Secondary Markers	Weight Split
Openness	Long words, type-token ratio, articles	Insight words, tentative language, prepositions	60% primary / 40% secondary
Conscientiousness	Achievement words, exclusion words, sentence length	Discourse markers, future tense, negation precision	60% / 40%
Extraversion	Social words, positive emotion, we-pronouns	Exclamation rate, informal register, short words	60% / 40%
Agreeableness	Positive emotion, affirmation, low swearing	Family words, hedging, inclusive language	60% / 40%
Neuroticism	I-pronouns, negative emotion, anxiety words	Past tense, sadness words, anger words	60% / 40%

Step 4: Normalize Scores and Compute Confidence Bands

Normalization:

Convert raw directional scores (-1.0 to +1.0) to a 0-100 scale centered at 50:

normalized_score = 50 + (raw_score * 50)

Where 50 = population average (indeterminate), 0 = extreme low-trait, 100 = extreme high-trait.

Confidence bands:

The confidence band width depends on corpus size and context diversity:

Corpus Size	Context Diversity	Confidence Band
1,000-2,000 words	Single context	+/- 25 points (extremely wide; results are suggestive only)
1,000-2,000 words	Multiple contexts	+/- 20 points
3,000-5,000 words	Single context	+/- 20 points
3,000-5,000 words	Multiple contexts	+/- 15 points
5,000-10,000 words	Multiple contexts	+/- 12 points
10,000+ words	Multiple contexts	+/- 10 points (practical floor -- text-based inference has inherent limits)

Interpreting positions:

Normalized Score	Position Label	Meaning
0-20	Very Low	Strong low-trait indicators; well below population average
20-35	Low	Below population average
35-50	Low-Average	Slightly below to average
50	Average / Indeterminate	No directional signal; could be truly average or insufficient data
50-65	High-Average	Slightly above average
65-80	High	Above population average
80-100	Very High	Strong high-trait indicators; well above population average

Midpoint scores (40-60) require special handling: If a trait scores in the 40-60 range, the confidence band likely spans from "low" to "high." Report this as "indeterminate" rather than "average." Average and indeterminate are different claims -- "average" implies knowledge of position; "indeterminate" correctly states the data cannot distinguish.

Step 5: Map Trait Positions to Writing Style Constraints

This is the critical translation step: converting trait scores into actionable writing constraints that enable voice replication.

Openness mapping:

Score Range	Writing Constraints
High (65+)	Use varied, sophisticated vocabulary. Include abstract concepts and intellectual framing. Employ tentative/exploratory language ("perhaps", "consider", "one might argue"). Reference ideas, culture, philosophy. Longer, more complex sentence structures.
Average (35-65)	Mix of concrete and abstract. Standard vocabulary without being either simplistic or deliberately ornate. Balanced sentence complexity.
Low (0-35)	Prefer concrete, familiar vocabulary. Short, direct sentences. Focus on tangible/practical topics. Avoid abstract theorizing. Straightforward phrasing.

Conscientiousness mapping:

Score Range	Writing Constraints
High (65+)	Structured, organized prose with clear paragraph progression. Use discourse markers ("therefore", "however", "in addition"). Achievement-oriented framing. Precise word choice with qualifying exclusions ("except", "unless"). Formal register.
Average (35-65)	Moderate structure. Mix of formal and informal. Some organizational markers.
Low (0-35)	Looser structure, more stream-of-consciousness. Casual tone. Shorter sentences. Fewer organizational markers. More present-tense, less future-planning language.

Extraversion mapping:

Score Range	Writing Constraints
High (65+)	Conversational, energetic tone. Frequent references to people, social events, group activities. Use "we/us" framing. Positive emotion words. Exclamation marks (sparingly). Shorter, punchier sentences. Informal register.
Average (35-65)	Balanced social and task-oriented content. Mix of "I" and "we." Moderate emotional expression.
Low (0-35)	Task-oriented, analytical tone. Fewer social references. More "I" than "we." Longer, more complex sentences. Restrained emotional expression. Formal or neutral register.

Agreeableness mapping:

Score Range	Writing Constraints
High (65+)	Warm, affirming tone. Hedge assertions ("I think", "it seems"). Express agreement and appreciation. Avoid blunt negative judgments. Inclusive language. Softened disagreements ("I see your point, but..."). No profanity.
Average (35-65)	Balanced directness and diplomacy. Occasional hedging. Can disagree openly but not harshly.
Low (0-35)	Direct, blunt tone. State opinions without hedging. Comfortable with disagreement and critique. May use stronger/coarser language. Challenge-oriented phrasing. Competitive framing.

Neuroticism mapping (inverted as Emotional Stability for constraint clarity):

Score Range (Neuroticism)	Writing Constraints
High N / Low Stability (65+)	More self-referential ("I feel", "I worry"). Express uncertainty and doubt. Use hedging about outcomes. Negative emotion vocabulary is natural and frequent. Past-tense reflection. Qualify positive statements.
Average (35-65)	Balanced emotional expression. Mix of positive and negative. Moderate self-reference.
Low N / High Stability (0-35)	Confident, assured tone. Fewer self-references. Primarily positive or neutral emotional vocabulary. Present-tense, forward-looking. Minimal anxiety or doubt markers. Certainty language ("clearly", "definitely").

Step 6: Generate the Personality-Informed Writing Profile

Synthesize the five trait positions and their writing constraints into a unified writing profile. The profile should resolve constraint conflicts between traits (e.g., high Openness suggests complex sentences, but high Extraversion suggests shorter sentences -- resolve toward moderate complexity with varied rhythm).

Profile structure:

## Writing Voice Profile

### Trait Summary
| Trait | Score | Position | Confidence |
|-------|-------|----------|------------|
| Openness | [X] | [label] | +/- [N] |
| Conscientiousness | [X] | [label] | +/- [N] |
| Extraversion | [X] | [label] | +/- [N] |
| Agreeableness | [X] | [label] | +/- [N] |
| Neuroticism | [X] | [label] | +/- [N] |

### Composite Writing Constraints
- **Vocabulary level:** [simple / moderate / sophisticated / varied]
- **Sentence complexity:** [short-direct / moderate / long-complex / varied-rhythm]
- **Emotional tone:** [restrained / balanced / expressive / warm / intense]
- **Social orientation:** [task-focused / balanced / people-focused]
- **Formality:** [casual / moderate / formal / context-dependent]
- **Assertion style:** [hedged / balanced / direct / blunt]
- **Temporal orientation:** [past-reflective / present-focused / future-oriented]
- **Self-reference density:** [low / moderate / high]

Resolving trait conflicts:

When two traits push writing constraints in opposite directions, resolve by:

1. Prioritize the stronger-signal trait (the one further from 50 with the narrower confidence band)
2. Document the tension in the profile ("High Openness suggests complex vocabulary, but high Extraversion favors conversational brevity -- resolve toward varied vocabulary in casual structures")
3. Never silently drop a constraint -- if a conflict exists, name it

Step 7: Write the Report

Write all findings to docs/analysis/15-big-five-personality.md.

Report Output Template

The final report MUST be written to docs/analysis/15-big-five-personality.md with this structure:

# Big Five (OCEAN) Personality Profile

## Methodology
- **Approach:** Linguistic marker frequency analysis mapped to Big Five personality dimensions
- **Corpus:** [N words scored, N documents/texts, date range, source description]
- **Marker framework:** LIWC-validated categories (Pennebaker & King 1999; Yarkoni 2010; Stachl et al. 2022)
- **Scoring method:** Directional composite scoring from marker frequencies, normalized to 0-100 scale
- **Confidence calibration:** Based on corpus size ([N] words) and context diversity ([single/multiple])

## Corpus Suitability Assessment
- **Word count:** [N] words ([sufficient / insufficient / marginal])
- **Language:** [English / mixed]
- **Authorship:** [Single / multiple / unknown]
- **Context diversity:** [Single context / multiple contexts -- list them]
- **Editing level:** [Natural / lightly edited / heavily edited]
- **Temporal span:** [Single occasion / multiple occasions over N days/months/years]
- **Overall suitability:** [Suitable / suitable with caveats / unsuitable]

## Trait Scores

| Trait | Raw Score | Normalized (0-100) | Position | Confidence Band | Interpretation |
|-------|-----------|--------------------|-----------|--------------------|----------------|
| Openness | [+/-X.XX] | [0-100] | [label] | +/- [N] | [1-sentence interpretation] |
| Conscientiousness | [+/-X.XX] | [0-100] | [label] | +/- [N] | [1-sentence interpretation] |
| Extraversion | [+/-X.XX] | [0-100] | [label] | +/- [N] | [1-sentence interpretation] |
| Agreeableness | [+/-X.XX] | [0-100] | [label] | +/- [N] | [1-sentence interpretation] |
| Neuroticism | [+/-X.XX] | [0-100] | [label] | +/- [N] | [1-sentence interpretation] |

## Linguistic Evidence per Trait

### Openness
- **Marker frequencies:** [table of measured indicator rates vs. corpus baseline]
- **Key evidence:** [most prominent indicators and their rates]
- **Confidence notes:** [what strengthens or weakens confidence in this score]

### Conscientiousness
[Same structure]

### Extraversion
[Same structure]

### Agreeableness
[Same structure]

### Neuroticism
[Same structure]

## Writing Style Constraints (for Voice Replication)

### Composite Profile
| Dimension | Constraint | Derived From |
|-----------|-----------|--------------|
| Vocabulary level | [constraint] | [trait(s)] |
| Sentence complexity | [constraint] | [trait(s)] |
| Emotional tone | [constraint] | [trait(s)] |
| Social orientation | [constraint] | [trait(s)] |
| Formality | [constraint] | [trait(s)] |
| Assertion style | [constraint] | [trait(s)] |
| Temporal orientation | [constraint] | [trait(s)] |
| Self-reference density | [constraint] | [trait(s)] |

### Trait Conflict Resolutions
[Document any conflicts between trait-derived constraints and how they were resolved]

### Exemplar Sentences
[3-5 sentences that demonstrate the constrained writing style, drawn from or inspired by the corpus]

## Limitations and Caveats
- Text-based personality inference captures tendencies, not diagnoses. The strongest individual LIWC-trait correlation is ~0.23 (Yarkoni, 2010).
- LIWC categories collectively explain ~5.1% of self-reported personality variance (Stachl et al., 2022). Results are probabilistic indicators, not deterministic measures.
- Writing context strongly affects language: the same person writes differently in email vs. social media vs. academic papers. Scores reflect the writing context as much as the person.
- [Corpus-specific limitations from Step 1]
- [Confidence band acknowledgment: scores of X and Y overlap at their confidence intervals, meaning they may not be truly different]
- These results should NOT be used for clinical assessment, hiring decisions, or any evaluative purpose. They are intended solely for writing style characterization.

## Population Baseline Comparison
- All scores are normed against a theoretical population mean of 50.
- Without a matched comparison corpus, absolute positions should be interpreted cautiously.
- The profile is most useful as a RELATIVE description (which traits are strongest/weakest for this author) rather than an absolute placement.

## References
- Pennebaker, J.W. & King, L.A. (1999). Linguistic styles: Language use as an individual difference. *Journal of Personality and Social Psychology*, 77(6), 1296-1312.
- Yarkoni, T. (2010). Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers. *Journal of Research in Personality*, 44(3), 363-373.
- Stachl, C., et al. (2022). The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and LIWC. *Psychological Bulletin*.
- Hirsh, J.B. & Peterson, J.B. (2009). Personality and language use in self-narratives. *Journal of Research in Personality*, 43(3), 524-527.
- Boyd, R.L. & Pennebaker, J.W. (2017). Language-based personality: A new approach to personality in a digital world. *Current Opinion in Behavioral Sciences*, 18, 63-68.
- Tausczik, Y.R. & Pennebaker, J.W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. *Journal of Language and Social Psychology*, 29(1), 24-54.

Good Patterns

• Use validated marker lists from meta-analytic research (Stachl et al. 2022, Yarkoni 2010) rather than intuition-based word lists
• Report confidence intervals on every trait score -- never present a point estimate without a band
• Treat results as tendencies ("the writing suggests moderate openness") not diagnoses ("this person is open")
• Map traits to writing constraints -- the practical output is a replicable writing profile, not a personality label
• Compare against population baselines -- raw marker frequencies without normalization are uninterpretable
• Document corpus limitations prominently, not as a buried footnote
• Score all five traits even when some are indeterminate -- an indeterminate score is information ("the data does not distinguish this trait")
• Resolve trait conflicts explicitly when mapping to writing constraints -- never silently drop conflicting evidence

Anti-Patterns

Anti-Pattern	Why It Fails	Instead
Claiming diagnostic accuracy	Text-personality correlations max at r = .23; 95% of variance is NOT explained	Report correlations, confidence bands, and the "tendencies not diagnoses" framing
Using fewer than 1,000 words	Below 1,000 words, marker frequencies are dominated by noise	Require 1,000-word minimum; flag 1,000-3,000 as low confidence
Treating OCEAN scores as fixed personality	Scores reflect THIS corpus in THIS context at THIS time	Frame as "writing-derived trait indicators" not "personality scores"
Ignoring context effects	A professional email corpus will score low-Extraversion regardless of actual personality	Document context, flag single-context corpora, note which traits are most context-dependent
Using unvalidated word lists	Intuitive word lists may not correlate with actual personality traits	Use LIWC-validated categories or lists derived from published research
Conflating online writing with offline personality	Online self-presentation is curated and context-specific	Note that writing-derived profiles reflect presented identity, not private experience
Presenting scores without confidence bounds	A score of 72 means nothing without knowing it could be 57-87	Always pair scores with confidence bands
Scoring only 1-2 traits	Partial profiles invite over-interpretation of the measured traits	Score all five; mark indeterminate traits as such
Binary trait classification	"Extraverted" vs "Introverted" loses the continuum	Use position labels (Low / Low-Average / Average / High-Average / High / Very High)
Ignoring the "kernel of truth" meta-analysis	Cherry-picking individual studies inflates expected accuracy	Anchor expectations to the meta-analytic effect sizes

Boundaries

This skill SHOULD produce:

• Trait position scores (0-100) for all five OCEAN dimensions with confidence bands
• Linguistic marker frequency tables showing evidence per trait
• Writing style constraints derived from trait positions
• Composite writing voice profile for style replication
• Explicit documentation of corpus limitations, confidence levels, and methodological caveats
• Written report at docs/analysis/15-big-five-personality.md

This skill should NOT:

• Diagnose personality traits, disorders, or clinical conditions
• Claim clinical validity or psychometric equivalence with validated personality instruments (NEO-PI-R, BFI-2)
• Be used for hiring, screening, admissions, or any evaluative decision about a person
• Present scores without confidence bounds
• Ignore that writing context affects language (professional email vs. casual social media vs. creative writing)
• Claim accuracy beyond what meta-analytic evidence supports (~5% of self-reported variance explained)
• Produce a profile from fewer than 1,000 words
• Treat a single writing context as representative of whole personality
• Conflate "presented writing identity" with "actual personality"

Insufficient Data Handling

digraph insufficient_data {
    rankdir=TB;
    node [shape=diamond, style=filled, fillcolor="#f0f0f0"];

    start [label="Total word count?", shape=diamond];
    tiny [label="< 1,000 words", shape=box, fillcolor="#ffcccc"];
    small [label="1,000-3,000 words", shape=box, fillcolor="#fff3cc"];
    moderate [label="3,000-5,000 words", shape=box, fillcolor="#ffffcc"];
    good [label="5,000+ words", shape=box, fillcolor="#ccffcc"];

    start -> tiny [label="< 1,000"];
    start -> small [label="1,000-3,000"];
    start -> moderate [label="3,000-5,000"];
    start -> good [label="5,000+"];

    tiny_action [label="STOP: Report insufficient data.\nDo NOT compute trait scores.\nReport only raw marker counts.", shape=box, fillcolor="#ffcccc"];
    small_action [label="Compute scores with\nLOW CONFIDENCE flag on every trait.\nConfidence band: +/- 20-25 points.\nSkip writing constraint mapping.", shape=box, fillcolor="#fff3cc"];
    moderate_action [label="Full analysis.\nModerate confidence.\nBand: +/- 15 points.\nMap writing constraints with caveats.", shape=box, fillcolor="#ffffcc"];
    good_action [label="Full analysis pipeline.\nBand: +/- 10-12 points.\nFull writing constraint mapping.", shape=box, fillcolor="#ccffcc"];

    tiny -> tiny_action;
    small -> small_action;
    moderate -> moderate_action;
    good -> good_action;
}

Condition	Action
Corpus < 1,000 words	STOP. Do NOT compute trait scores. Report only: raw word count, language, and a statement that the corpus is too small for personality inference. List raw marker frequencies without interpreting them as trait indicators.
Corpus 1,000-3,000 words	Compute scores but flag EVERY trait as "low confidence." Use +/- 20-25 point confidence bands. Skip writing constraint mapping (too unreliable). Report scores as "preliminary indicators only."
Corpus 3,000-5,000 words	Full analysis with moderate confidence. Use +/- 15 point confidence bands. Map writing constraints but caveat that they reflect limited data.
Corpus 5,000+ words	Full analysis pipeline. Use +/- 10-12 point bands. Reliability improves marginally beyond 10,000 words due to inherent method limits.
Single-context corpus	Widen confidence bands by 5 points. Note prominently that trait scores reflect the writing context as much as the person. Extraversion and Agreeableness are most affected by context.
Highly uniform topic	All writing about one narrow topic (e.g., all game reviews) constrains vocabulary and tone. Openness and Conscientiousness scores are least reliable here. Flag in report.
Multiple traits score 40-60	If 3+ traits are in the 40-60 range, the corpus may lack sufficient personality signal. Report as "insufficient differentiation" rather than "average personality." Consider whether context uniformity is suppressing signal.
All traits cluster on one side	If all five traits score above 65 or below 35, check for a systematic bias (e.g., all positive/enthusiastic writing inflates E, A, and O simultaneously). Note the clustering and investigate whether context explains it.

Common Mistakes

Mistake	Fix
Reporting "Extraversion: 73" without confidence band	Always report "73 +/- 15" or "73 (range: 58-88)"
Using OCEAN as a personality diagnosis	Frame as "writing-derived trait indicators reflecting linguistic patterns in this corpus"
Analyzing a corpus under 1,000 words	Stop and report insufficient data. Do not compute scores.
Treating a single-context corpus as personality ground truth	Flag single-context prominently; note which traits are most context-dependent
Scoring only the "interesting" traits	Score all five. Indeterminate is information, not absence.
Mapping writing constraints from low-confidence scores	Only map writing constraints when confidence is moderate or higher (3,000+ words)
Ignoring that I-pronoun frequency has multiple interpretations	High I-pronouns correlate with both Neuroticism AND depression AND self-awareness AND intimate writing style. Do not over-interpret.
Presenting the profile as stable/permanent	Personality traits are relatively stable, but linguistic expression of them varies by context, mood, and audience
Using the profile for evaluative judgments	This is a writing style tool, not a character assessment tool
Skipping the population baseline comparison	Without baseline comparison, raw scores are uninterpretable. Always note that 50 = theoretical population mean.

References

• Pennebaker, J.W. & King, L.A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296-1312.
• Yarkoni, T. (2010). Personality in 100,000 Words. Journal of Research in Personality, 44(3), 363-373.
• Stachl, C., et al. (2022). The kernel of truth in text-based personality assessment: A meta-analysis. Psychological Bulletin.
• Hirsh, J.B. & Peterson, J.B. (2009). Personality and language use in self-narratives. Journal of Research in Personality, 43(3), 524-527.
• Boyd, R.L. & Pennebaker, J.W. (2017). Language-based personality: A new approach. Current Opinion in Behavioral Sciences, 18, 63-68.
• Tausczik, Y.R. & Pennebaker, J.W. (2010). The psychological meaning of words. Journal of Language and Social Psychology, 29(1), 24-54.
• LIWC-22 Manual: Development and Psychometrics.
• IBM Watson Personality Insights: Validation and Methodology.
• Frontiers in Psychology: Linguistic measures of personality in group discussions (2022).