Classify or Die Trying: Teaching Machines to Sort Human Nonsense

July 11, 2025

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A tragicomic journey through newsgroups, natural language, and near-miss predictions

“Natural language processing is the field of making machines feel as confused as you do.”
— Anonymous, or possibly your classifier

🧭 Act I: Unearthing the Corpus

Some datasets are majestic.
Others are monstrous.

And then there’s the 20 Newsgroups —
a glorious fossil bed of 1990s internet discourse, preserved in plaintext and passive aggression.

This dataset is made up of 20 distinct newsgroups. Each is a lovingly curated firepit of hyper-specific content, such as:

talk.religion.misc – where theology meets uppercase rage
comp.graphics – where OpenGL meets driver-induced despair
sci.space – where NASA dreams go to be overcorrected
rec.sport.hockey – where Canadians argue about penalties with the passion of 1,000 suns

🔧 Act II: Building the Classifier That Didn’t Ask for This

In this tutorial, you take the role of a machine learning sadist. You hand the model these forums and say:

“Sort this. I dare you.”

Here’s the battle plan:

from sklearn.datasets import fetch_20newsgroups
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier

categories = ['sci.space', 'talk.religion.misc', 'comp.graphics', 'rec.sport.hockey']

train_data = fetch_20newsgroups(subset='train', categories=categories)
test_data = fetch_20newsgroups(subset='test', categories=categories)

clf = Pipeline([
    ('vectorizer', TfidfVectorizer()),
    ('classifier', SGDClassifier(loss='hinge', max_iter=1000, tol=1e-3))
])

clf.fit(train_data.data, train_data.target)

Congratulations, you now have a working text classifier.
It doesn’t understand you.
It doesn’t want to understand you.
But it will label your chaos anyway.

🧠 Act III: Feeding the Beast

Let’s throw it some modern content and see how it reacts.

🔍 Example 1: Reddit Thread

“I told my boss I was quitting to start a moon-based religion run on JavaScript.”

Prediction: sci.space  
Confidence: 66%  
Notes: Triggered by “moon.” Hesitated on “religion.” Short-circuited on “JavaScript.”

🔍 Example 2: Slack Message in #random

“Can God make a pointer He cannot dereference?”

Prediction: comp.graphics  
Confidence: 84%  
Notes: Interpreted as a GPU theology ticket. Escaped segmentation fault. Barely.

🔍 Example 3: NHL Commentator, Mid-Rage

“Where’s the damn ref? That’s clearly icing!”

Prediction: talk.religion.misc  
Confidence: 43%  
Notes: Mistook “icing” for sacramental ritual. Recommends confession and review of the rulebook.

🤖 Act IV: When Classifiers Break Down

Here’s a real transcript from one of the misclassified posts:

“Jesus saves... but Gretzky scores on the rebound.”

→ Predicted: comp.graphics  
Actual: rec.sport.hockey  
Confidence: 91%

Notes: Model mistook "saves" as file I/O. This is why AI should not do sports commentary.

Another:

“My driver update broke my GIF renderer and now the crucifix spins.”

→ Predicted: talk.religion.misc  
→ Actual: comp.graphics  
→ Confidence: 38%

Notes: It’s not wrong. It’s just spiritually overwhelmed.

🎨 Act V: Confusion Matrix of Despair

Here’s what happens when the model tries to distinguish between forums about religion and forums about space exploration:

Truth / Prediction	sci.space	talk.religion.misc	comp.graphics	rec.sport.hockey
sci.space	✅	🙏	❌	🏒
talk.religion.misc	🙃	✅	❌	🙃
comp.graphics	❌	❌	✅	❌
rec.sport.hockey	🏒	🙏	❌	✅

Legend:

✅ = Correct

❌ = Model asked to be unplugged

🙏 = Theology-related trauma

🏒 = Puck confusion

🔚 Act VI: So What Have We Learned?

Machines can learn to classify text.
But no machine can truly understand Usenet humans.
The difference between comp.graphics and talk.religion.misc might just be… formatting.

🎤 Epilogue: What You Can Do With This

You could:

Classify your email inbox.
Auto-detect flame wars in Slack.
Build a chatbot that gives spiritual advice during GPU crashes.
Or just admire the madness of a world where a model thinks CSS stands for Christ’s Sacred Scripture.

🧵 TL;DR

Text classification is beautiful.
And brittle.
And hilarious.

Now go forth, train a model, and let it judge humanity — one forum post at a time.