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."