How to perform text classification with machine learning

Short answer

Text classification assigns predefined categories to documents using statistical or neural models that generalize from labeled training examples.

Steps

1. Define taxonomy and collect representative labeled examples for each class.
2. Clean and tokenize text, handling special characters and stop words appropriately.
3. Vectorize documents using count vectors, TF-IDF, or dense embeddings.
4. Train a model ranging from naive Bayes to deep neural networks depending on data size.
5. Optimize hyperparameters and validate on unseen data before deployment.

Tips

• Start with simple linear models on TF-IDF features to establish a strong baseline.
• Use n-grams to capture local word order missed by unigram representations.
• Fine-tune transformer models when labeled data exceeds a few thousand examples.
• Apply label smoothing to prevent overconfident predictions.

Common issues

• High-dimensional sparse features causing overfitting with limited data.
• Class imbalance skewing predictions toward majority categories.
• Overlapping class definitions creating ambiguous labels.
• Preprocessing mismatches between training and inference pipelines.

Example

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Linear(256, 10)
)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

This snippet defines a simple neural network with dropout for regularization, a cross-entropy loss, and the Adam optimizer in PyTorch.