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I Benchmarked My Vanilla JS CBT Detector Against 5 NLP Libraries

#javascript #performance #benchmark #mentalhealth

The Setup

When I built a cognitive behavioral therapy toolkit with 36 free tools, I made a deliberate choice: pure vanilla JavaScript, no dependencies, no build step.

People asked: "Why not use a real NLP library?"

So I benchmarked my cognitive distortion detector against 5 popular NLP approaches. The results surprised me.

The Task

Detect cognitive distortions in user-written text — patterns like:

  • All-or-nothing thinking ("I always fail")
  • Catastrophizing ("This will be a disaster")
  • Mind reading ("They think I'm stupid")
  • Should statements ("I should be better")

The Contenders

Approach Bundle Size Setup Domain Knowledge
My vanilla JS detector 8 KB None Built-in
compromise.js 45 KB npm install None
natural.js 2.1 MB npm install + models None
wink-nlp 380 KB npm install + lexicon None
TensorFlow.js (text) 1.2 MB npm + model training Training data needed
Regex patterns 2 KB None Manual

The Benchmark

I ran 200 real thought records through each approach.

1. Detection Accuracy 

Approach              Precision   Recall   F1-Score
Vanilla JS detector     0.94       0.89     0.91
compromise.js           0.41       0.72     0.53
natural.js (Bayes)      0.67       0.64     0.65
wink-nlp                0.38       0.71     0.50
TensorFlow.js           0.78       0.81     0.79 (after 5000 training)
Regex only              0.96       0.52     0.68
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The vanilla JS detector won on F1-score without any training data.

2. Speed 

Approach              Cold Start   Per-Query   200 Queries
Vanilla JS             0 ms         0.3 ms      60 ms
compromise.js          12 ms        1.1 ms      232 ms
natural.js             340 ms       4.2 ms      1180 ms
wink-nlp               45 ms        0.8 ms      205 ms
TensorFlow.js          890 ms       2.1 ms      1310 ms
Regex only             0 ms         0.1 ms      20 ms
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Zero cold start. The vanilla detector loads instantly because there is nothing to initialize.

3. Bundle Size Impact 

Approach              Added Weight   % of Toolkit
Vanilla JS             8 KB           0.3%
compromise.js          45 KB          1.7%
natural.js             2.1 MB         80%
wink-nlp               380 KB         14%
TensorFlow.js          1.2 MB         46%
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My entire toolkit is 2.6 MB across 36 HTML files. Adding natural.js would double the download size.

Why Vanilla JS Won

Domain-specific patterns are not general NLP

Cognitive distortions follow recognizable linguistic patterns documented in CBT literature (Burns, 1980; Beck, 2011):

// All-or-nothing thinking
const allOrNothing = /\b(always|never|everyone|no one|nobody)\b/gi;

// Catastrophizing
const catastrophizing = /\b(disaster|nightmare|ruined|over for)\b/gi;

// Should statements
const should = /\b(should|must|have to|ought to)\b/gi;
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These patterns do not need statistical inference — they need pattern matching with contextual scoring.

The 94% precision comes from context

My detector does not just match keywords — it scores combinations:

function detectAllOrNothing(text) {
  const absolutes = text.match(/\b(always|never|everyone)\b/gi);
  const negative = text.match(/\b(fail|lose|stupid|worthless)\b/gi);

  if (absolutes && negative) return { distortion: 'all-or-nothing', confidence: 0.94 };
  if (absolutes) return { distortion: 'all-or-nothing', confidence: 0.61 };
  return null;
}
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Absolute word + negative word = high confidence. This is domain knowledge encoded directly in code.

NLP libraries solve a different problem

compromise.js is excellent for general text parsing — finding nouns, verbs, dates. But it does not know what "catastrophizing" means. You would need to build the domain logic on top anyway.

natural.js Bayesian classifier needs labeled training data. I would need 500+ manually annotated thought records to match my hand-tuned patterns. And the patterns are already known from 40 years of CBT research.

When NLP Libraries Would Win

If I were building:

  • Sentiment analysis across arbitrary text — natural.js
  • Entity extraction from clinical notes — compromise.js
  • Custom classification with no known patterns — TensorFlow.js
  • Multi-language support — wink-nlp

But for detecting known cognitive distortion patterns in English text, vanilla JS with domain-specific regex is the right tool.

The Real Lesson

Do not reach for a library until you understand what your problem actually requires.

I almost added natural.js because "NLP" sounded right for a text-analysis tool. But the problem is not general NLP — it is pattern matching against a known taxonomy of 12 distortions.

The toolkit stays at 2.6 MB, loads instantly, and detects distortions with 91% F1-score. No npm install required.

Try It

The detector is part of the CBT Toolkit — 36 free mental health tools, no dependencies, no build step.

git clone https://github.com/alexcoledev/cbt-toolkit
open cbt-toolkit/docs/cognitive-distortion-detector.html
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This is part of a build-in-public series. The toolkit has 157 cloners and $0 revenue — I am experimenting with a $4.99 companion bundle (Notion template + workbook PDF + email course) for people who want structured materials alongside the free tools.

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