<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Text-Cleaning on DATATWEETS</title><link>https://datatweets.com/tags/text-cleaning/</link><description>Recent content in Text-Cleaning on DATATWEETS</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2026 Datatweets</copyright><lastBuildDate>Sat, 11 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://datatweets.com/tags/text-cleaning/index.xml" rel="self" type="application/rss+xml"/><item><title>Cleaning Text Data for NLP: A Practical Guide to Messy Real-World Text</title><link>https://datatweets.com/tutorials/cleaning-text-data-for-nlp/</link><pubDate>Sat, 11 Jul 2026 00:00:00 +0000</pubDate><guid>https://datatweets.com/tutorials/cleaning-text-data-for-nlp/</guid><description>Real text data breaks in ways numeric data doesn&amp;rsquo;t. This guide walks through cleaning a genuinely messy app-review dataset in pandas — missing and blank text fields, encoding artifacts, stray HTML, inconsistent casing and whitespace, and near-duplicate reviews — until it&amp;rsquo;s ready to hand to a tokenizer.</description></item></channel></rss>