How many training examples do I need per category?

Minimum 50-100 for simple tasks with traditional ML. Deep learning wants more (500+). LLMs can work with fewer via few-shot learning. Imbalanced classes need special handling.

What if my categories overlap?

Use multi-label classification where items can belong to multiple categories. If categories are genuinely unclear, consider refining your category definitions.

Should I use traditional ML or LLMs for classification?

Traditional ML is faster, cheaper, and often sufficient. LLMs excel when: categories are complex/nuanced, you have little training data, or you need to handle novel categories.

How do I handle new categories that emerge over time?

Options: retrain periodically, use hierarchical categories with "other" catch-all, implement zero-shot classification with LLMs, or build a system that flags uncertain cases for review.

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Text Classification

NLP task of assigning predefined categories to text. Used for spam detection, sentiment analysis, topic categorisation, and intent recognition.

In-Depth Explanation

Text classification automatically assigns categories or labels to text documents. It's one of the most practical and widely-deployed NLP applications in business.

Classification types:

Binary: Two classes (spam/not spam)
Multi-class: Multiple mutually exclusive classes
Multi-label: Multiple applicable labels per document
Hierarchical: Nested category structures

Common business applications:

Email spam filtering
Customer feedback categorisation
Support ticket routing
Document organisation
Intent detection for chatbots
Compliance flagging

Approaches:

Traditional ML: Naive Bayes, SVM, Random Forest
Deep learning: CNNs, RNNs, transformers
LLM-based: Prompting or fine-tuning large models
Hybrid: Combining approaches for best results

Business Context

Text classification automates tedious categorisation work - routing emails, tagging documents, prioritising tickets. It scales human judgment to millions of documents.

How Clever Ops Uses This

We implement text classification systems for Australian businesses to automate document routing, customer inquiry classification, and content moderation.

Example Use Case

"Automatically routing incoming customer emails to the right department based on content - billing, technical support, sales, or general inquiries."

Frequently Asked Questions

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