7 Readability Features for Your Next ML Model

What’s Happening

Let’s talk about Unlike fully structured tabular data, preparing text data for ML models typically entails tasks like tokenization, embeddings, or sentiment analysis.

7 Readability Features for Your Next ML Model By Iván Palomares Carrascosa on in Practical ML 0 Post In this article, you will learn how to extract seven useful readability and text-complexity features from raw text using the Textstat Python library. Topics we will cover include: How Textstat can quantify readability and text complexity for downstream ML tasks. (shocking, we know)

How to compute seven commonly used readability metrics in Python.

The Details

How to interpret these metrics when using them as features for classification or regression models. While these are undoubtedly useful features, the structural complexity of text or its readability, for that matter can also constitute an insanely informative feature for predictive tasks such as classification or regression.

Textstat , as its name suggests, is a lightweight and intuitive Python library that can help you obtain statistics from raw text. Through readability scores, it provides input features for models that can help distinguish between a casual socials post, a childrens fairy tale, or a philosophy manuscript, to name a few.

Why This Matters

This article introduces seven insightful examples of text analysis that can be easily conducted using the Textstat library. Before we get kicked off, make sure you have Textstat installed: pip install textstat 1 pip install textstat While the analyses described here can be scaled up to a large text corpus, we will illustrate them with a toy dataset consisting of a small number of labeled texts. Bear in mind, but, that for downstream ML model training and inference, you will need a sufficiently large dataset for training purposes.

As AI capabilities expand, we’re seeing more announcements like this reshape the industry.