Glossary

Text Classification

What is Text Classification?

Text classification automatically assigns texts to predefined categories, for example emails by request type, reviews by sentiment or documents by topic. Previously this required training dedicated models on labelled examples; today large language models can often assign categories from a description alone. This makes it possible to pre-sort enquiries and route them to the right team faster.

How KI.ordo helps

The KI.ordo template “Classify text” uses the AI router building block, which assigns inputs to up to eight freely chosen categories.

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Matching templates

Classify a textAssigns a text to a category (question, complaint, order, other) and outputs it together with the text. The categories in the AI router can be changed freely; simply connect new categories to the output.View template →

Topics

NLP
Classification
Automation

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Related terms

Automatic classification

Automatic classification assigns texts, documents or records to predefined categories without manual sorting. Traditionally, models learn this from labelled examples; large language models can also assign categories based on a description alone. Typical uses include sorting incoming emails by request type, assigning receipts or flagging urgent support tickets. For important cases, spot checks by people are advisable.

Classification
Automation
+1

Template

Classifier

A classifier is a model or method that assigns inputs to one of several predefined categories. Classic variants such as decision trees or naive Bayes are trained on labelled examples; language models can also sort texts based on a description of the categories. Typical uses include spam detection, sorting customer enquiries by topic and categorising receipts. Quality is measured with metrics such as accuracy, precision and recall.

Classification
Machine learning
+1

Template

Sentiment analysis

Sentiment analysis automatically determines the mood or attitude a text expresses – usually positive, neutral or negative, sometimes finer-grained emotions. Businesses use it to evaluate large numbers of product reviews, support emails, surveys or social media posts and quickly see where customers are satisfied and where problems lie. Irony, sarcasm and mixed statements such as “fast delivery, disappointing quality” remain difficult, so samples should be checked by people.

NLP
customer feedback
+1

Template