What it does

Text Classification assigns one or more classes to a document according to their content. Classes are selected from a previously established taxonomy (a hierarchy of categories or classes). This app takes care of all preprocessing tasks (extracting text, tokenization, stopword removal, and lemmatization) required for automatic classification.

This app supports a variety of text classification scenarios like:

  1. Binary classification like spam filtering (HAM, SPAM) or simple sentiment analysis (POSITIVE, NEGATIVE).
  2. Multiple class classification like selecting one category among several alternatives - movie genre classification (thriller, terror, romantic, etc.)
  3. Multilabel categorization - assigning all categories that apply to a single document
  4. Complex taxonomy categorization - assigning categories arranged in a multilevel taxonomy

The app combines statistical document classification with rule-based filtering, which allows us to obtain a high degree of precision in a wide range of environments.

Try it out with some sample text!

How I built it

Using Forge UI and API

Challenges I ran into

Getting started with Forge

Accomplishments that I'm proud of

A working app

What I learned

Forge Atlassian developer products

What's next for Text classification for JIRA

Better visualization of app response output. Same feature for Confluence.

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