Literary Analysis
A computational reading of The Becoming
What can numbers tell us about a journey through darkness into light? This analysis examines the 26 poems of The Becoming through the lens of computation — tracing sentiment, vocabulary, and the shifting pronouns that mark Sarah's movement from isolation to connection.
The data reveals what the reader already feels: a collection that begins in pain and ends in love, with the turning point somewhere in the middle of Act III.
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Emotional Arc
Each point below is a poem. The trajectory traces Sarah's emotional journey from the depths of Act I to the warmth of Act IV — a measurable shift from negative to positive sentiment across 26 poems.
Poles
Voices from Each Act
The most emotionally charged line from each act, selected computationally.
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The Healing Trajectory
Each poem contains a mix of trauma language (pain, dark, alone, broken) and healing language (light, grow, love, safe). The ratio between them traces the arc of recovery.
The Shift
From I to We
One of the most striking patterns in The Becoming is the pronoun shift. The early poems are saturated with I, me, my — the language of isolation. By Act IV, you, we, our begin to appear — the grammar of connection.
The Grammar of Connection
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Emotional Palette
Which emotions dominate each act? This heatmap shows the density of five emotional vocabularies — joy, sadness, anger, fear, and love — normalized per 100 words.
Vocabulary Richness
The ratio of unique words to total words in each poem — a measure of linguistic diversity. Higher values suggest more varied, exploratory language.
The Voices Within
Sarah's poems contain six detectable voices — distinct modes of expression that surface across the collection. The child speaks from fear and smallness. The protector builds walls. The angry voice burns. The sensual reaches for touch. The spiritual seeks meaning. The analytical tries to understand. Their relative presence shifts dramatically across the four acts.
Each Voice, in Its Own Words
Maybe y'all were just a little off cue
Child voice — The Database, Act II
Protector voice — Imaginary Prisons, Act I
Angry voice — Drought, Act I
Sensual voice — Bathtub Blues, Act I
As he slipped into a peaceful slumber
Spiritual voice — Imaginary Prisons, Act I
I realize shit and write it down to be forgotten
Analytical voice — The Database, Act II
Polyphony
The Art of the Question
How often does Sarah ask a question? The density of question marks reveals which acts are driven by interrogation — the turning inward of self-examination — and which have found their answers.
Questioning
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All Poems
Click any row to read the poem.
| # | Title | Act | Sentiment | Words | Vocab |
|---|---|---|---|---|---|
| 1 | Bathtub Blues | Act I | -0.14 | 59 | 80% |
| 2 | Drought | Act I | +0.04 | 149 | 70% |
| 3 | Imaginary Prisons | Act I | +0.08 | 191 | 63% |
| 4 | Empty Bar Seat | Act I | -0.02 | 241 | 61% |
| 5 | Make Something of Yourself, Sarah | Act II | -0.04 | 49 | 82% |
| 6 | The Database | Act II | +0.17 | 143 | 71% |
| 7 | Empty Calories | Act II | -0.08 | 147 | 67% |
| 8 | The Need to Be Understood | Act II | -0.13 | 140 | 67% |
| 9 | Patience | Act II | +0.39 | 44 | 89% |
| 10 | Lost in Thought | Act II | -0.31 | 67 | 73% |
| 11 | Metamorphosis | Act II | -0.03 | 65 | 82% |
| 12 | A New Era | Act III | +0.16 | 137 | 66% |
| 13 | Woke Me Up | Act III | +0.06 | 82 | 82% |
| 14 | Insignificant to Magnificent | Act III | +0.18 | 178 | 68% |
| 15 | The Web | Act III | +0.16 | 134 | 76% |
| 16 | Dearest Poetry | Act III | -0.05 | 149 | 70% |
| 17 | The Excitement of Discovery | Act III | +0.21 | 255 | 64% |
| 18 | Love n' Leave | Act IV | -0.01 | 214 | 63% |
| 19 | The First of Many | Act IV | +0.33 | 170 | 69% |
| 20 | For Kenneth | Act IV | +0.09 | 57 | 93% |
| 21 | Tortoise Heart | Act IV | -0.12 | 39 | 95% |
| 22 | Blissful Tempest | Act IV | +0.33 | 161 | 65% |
| 23 | We Jump | Act IV | +0.14 | 89 | 75% |
| 24 | Transplanted | Act IV | +0.19 | 46 | 89% |
| 25 | Safe to Thrive | Act IV | +0.38 | 110 | 75% |
| 26 | The Light of Morning | Act IV | +0.42 | 90 | 81% |
At a Glance
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Methodology
Sentiment analysis uses TextBlob's pattern-based approach, measuring polarity on a scale from −1 (negative) to +1 (positive). Healing and trauma word counts are based on curated lexicons. Pronoun analysis counts exact word matches. Emotion categories use partial stem matching. All analysis is computational and represents one lens through which to view these poems — the numbers illuminate patterns, but the poetry speaks for itself.