AI & Machine Learning

I Built a Text Readability Scorer API with Flesch-Kincaid, Gunning Fog and More - Day 4 of 21

Ali Nemati8 hours ago1m read2 views0 listens

This article describes a project where the author, over the course of 21 days, builds and deploys 21 different APIs, with each API focusing on a specific functionality related to text analysis or validation. The particular focus here is on Day 4's API: Text Readability Scorer.

Key Features of Text Readability Scorer

Endpoints:
- /analyze: Analyzes the readability and complexity of given text.
- /compare: Compares two versions of a text to determine which one is more readable.
- /vocabulary: Provides vocabulary analysis, including unique words, lexical diversity, and word frequency.
Metrics:
- Flesch Reading Ease: A score indicating how easy the text is to read (higher scores are easier).
- Grade Level: The US grade level required to understand the text.
- Lexical Diversity: Ratio of unique words to total words, indicating vocabulary richness.
Additional Features:
- Detection of passive voice usage.
- Classification of word types such as transition words and hedge words (words that soften or qualify statements).

Technical Details

Runtime: Node.js + Express
**Hosting

Read the full article at DEV Community

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