What is Text Classification? - Definition & Meaning
Learn what text classification is, how AI automatically categorizes text, and why it is essential for email routing, tagging, and document processing.
Definition
Text classification is an NLP task where texts are automatically assigned to one or more predefined categories. It is one of the most common and useful NLP applications.
Technical explanation
Text classification includes binary classification (spam/not spam), multi-class (topic, intent), and multi-label (multiple tags per document). Methods: Naive Bayes, SVM, and modern transformers (BERT, RoBERTa) with a classification layer. Feature extraction: bag-of-words, TF-IDF, or encoder output. Fine-tuning on labeled data is standard. Use cases: email routing, support ticket categorization, content tagging, topic modeling, and intent detection for chatbots.
How AVARC Solutions applies this
AVARC Solutions builds text classification for email routing, ticket categorization, content tagging, and intent detection. We use transformer models and train on client-specific categories and Dutch-language data.
Practical examples
- An email system classifying incoming messages as "sales", "support", or "general" for automatic routing.
- A help desk tagging tickets by type (technical, billing, complaint) and priority based on content.
- A content platform automatically tagging articles with topics for search and recommendations.
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