Text Classification Service: Sort Documents and Comments into Your Categories

What this service covers

• Designing a label scheme with clear definitions
• Manual labelling of a training sample
• Classifiers based on logistic regression, SVM or transformer models
• Multi-label classification where a text fits several categories
• Evaluation with precision, recall, F1 and a confusion matrix
• Handling class imbalance and ambiguous texts
• Delivery of labelled data or a reusable script

Why build a classifier

Reading and tagging thousands of comments, complaints or documents by hand is slow and inconsistent. A classifier learns from a labelled sample and applies the same rules to everything else. It is useful for routing support tickets, tagging survey comments, coding open-ended responses or sorting documents by type.

A careful process

Success starts with the labels. We refine category definitions so human labellers agree, label a sample, then train and test models on data they have not seen. We report accuracy by category, show where the model confuses labels and set aside uncertain cases for human review. Where accuracy is too low, we say so instead of overselling.

Supervised versus discovery

Classification needs predefined categories. If you do not know the categories yet, begin with topic modelling or content analysis to find them. Classification can be combined with sentiment analysis to tag both subject and tone.

Delivery

You get the labelled dataset and, if wanted, a script or notebook to classify new data. For survey comments, see survey and social media text analysis.

How the work runs

1. You share the texts and your category list.
2. We refine labels and code a training sample.
3. We train, test and tune models.
4. You receive labelled data, accuracy results and optional scripts.

What you receive

• Labelled dataset
• Evaluation report with precision, recall and F1
• Confusion matrix and error analysis
• A reusable script or notebook if requested
• Guidance on reviewing uncertain cases