Computational Text Analysis
Uncover unconscious patterns in human language by treating literature, emotion, and dialect as empirical data. Use statistical and computational tools to profile authorial fingerprints, track narrative arcs, and map regional speech.
Like Duolingo, but for Computational Text Analysis. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
A short one: 12 levels across 2 sections, about 24 minutes end to end, roughly 5 days at five minutes a day. It moves through Profiling Voice and Authorship and Mapping Sentiment and Dialect. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Computational Text Analysis
- Function words reflect involuntary cognitive habits rather than deliberate topic choices.
- Content words change dramatically across topics while function words remain stable across texts.
- Cognitive psychology shows prepositions and articles escape conscious revision during writing.
- Absolute token counts scale linearly with document length, artificially inflating counts in larger texts.
- Normalizing frequencies per thousand words prevents document length from masking an author's true habit.
- Computational text profiles require relative rates to enable fair statistical distance calculations.
- Zipf's power law causes a few head terms to dwarf the variance of all other informative words.
- Applying a log transformation or high/low frequency threshold dampens statistical noise from rare words.
- Uncalibrated rare words distort distance metrics by acting as accidental, one-off outliers.
- Sentence-length distributions quantify syntactic complexity independent of chosen vocabulary.
- Punctuation ratios capture structural cadence and rhetorical pacing across literary works.
- Punctuation profiling bypasses lexical substitution by measuring grammatical architecture directly.
- Lemmatization strips inflectional suffixes, erasing distinct tense and mood preferences.
- Collapsing plural or archaic endings into base roots eliminates subtle grammatical tics.
- Topical classifiers benefit from stem reduction, but authorship attribution relies on inflectional choices.
- Top frequent words are non-thematic function words like prepositions and conjunctions.
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Computational Text Analysis the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
Why do stylometrists rely on function words rather than topic vocabulary to profile an author?
Get it right to open this lesson and 11 more in the app.
Where Computational Text Analysis takes you
- 1
Profiling Voice and Authorship
- Isolating Stylistic Fingerprints with Word Frequencies
- Attributing Authorship with Distance Metrics
- 2
Mapping Sentiment and Dialect
- Mapping Narrative Arcs and Emotional Tone
- Geolocating Dialects and Spatial Language
2 sections · 4 units · 12 levels. Built to play, not to enroll.
You pick the voice
Computational Text Analysis is taught in the The Professor style: clear, structured, thorough. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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Start Computational Text Analysis today.
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