Universal Data Tool
  • Universal Data Tool Docs
  • Installation
  • Running On-Premise
  • Collaborative Labeling
  • Building and Labeling Datasets
    • Image Segmentation
    • Image Classification
    • Text Classification
    • Named Entity Recognition
    • Entity Relations / Part of Speech Tagging
    • Audio Transcription
    • Data Entry
    • Video Segmentation
    • Composite Interfaces
    • Landmark / Pose Annotation
  • Importing Data
    • Upload or Open Directories
    • Import File URLs
    • Import COCO Images
    • Import from Google Drive
    • Import from AWS S3 Bucket
    • Import from CSV or JSON
    • Import using AWS Cognito
    • Import Text Snippets
  • The Format .udt.json
    • What is the .udt.json format?
    • What is the .udt.csv format?
  • Machine Learning
    • Jupyter Notebook Integration
    • Import Datasets into Pandas
    • Fast.ai
      • Fast.ai Image Classification
      • Fast.ai Image Segmentation
  • Integrate with Any Web Page
    • Integrate with the Javascript Library
    • Getting Started with React
  • Train your Workforce
    • Getting Started with UDT Courses
  • Frequently Asked Questions
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On this page
  • Setup the Dataset
  • Import Data
  • Label your Data (with friends!)
  • Export and Use

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  1. Building and Labeling Datasets

Text Classification

Classify text using the Universal Data Tool

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Last updated 4 years ago

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Setup the Dataset

Navigate to udt.dev and click "New File"

Then select the Text Classification button from the Setup > Data Type page.

You can now configure the interface you'd like for you Text Classification dataset by adding any classifications you'd like to display per sample, as well as configure if multiple classifications are allowed to be selected.

Import Data

You can use any of the following methods to import text data. If you're just getting started, you can quickly create a dataset using the COCO Images method!

  • Import Text Snippets

  • Import from CSV or JSON

Label your Data (with friends!)

Go to the Label tab to begin labeling data. See the Collaborative Labeling Guide to label with friends or a team of your labelers.

Export and Use

Text Classification can easily be exported and read as a CSV or JSON file.

For example, if you export as a CSV you'll get a table that looks like the following:

path

document

annotation

samples.0

This has made me so happy. I love it.

positive_sentiment

samples.1

I hate this thing. It's so bad.

negative_sentiment

Click "New File" on udt.dev
Text Classification Configuration
An example Text Classification interface