Best Practices

Robust data preprocessing

In this lesson, we’ll cover some best practices to adopt when dealing with irrelevant text data. We’ll start by covering robust data preprocessing, which involves handling irrelevant text data by cleaning and transforming the data into a format that can be effectively analyzed. This might mean undertaking several steps, such as tokenization, stopword removal, stemming or lemmatization, and noise removal from the text. Here’s a code example that explores robust data preprocessing using NLTK: