10 Text Cleaning Technics You Need in Python

Ten Python technics to clean text shared for your quick reference. Raw Text has unwanted hidden data, and as an analytics engineer, you need to remove these hidden unwanted tags.


10 Technics to Clean Text

  1. Removing HTML tags
  2. Tokenization
  3. Removing unnecessary tokens and stopwords
  4. Handling contractions
  5. Correcting spelling errors
  6. Stemming
  7. Lemmatization
  8. Tagging
  9. Chunking
  10. Parsing

10 Technics to Clean Text in Python
10 Technics to Clean Text in Python


Data is the prime source for text analytics projects, which will then feed to Machine Learning and Deep Learning systems.



1. Removing HTML tags

The unstructured text contains a lot of noise if you use techniques like web scraping or screen scraping to retrieve data from web pages, blogs, and online repositories. 

HTML tags, JavaScript, and Iframe tags typically don't add much value to understanding and analyzing text. Our purpose is we need to remove HTML tags and other noise.



2. Tokenization

  • Tokens are independent and minimal textual components. And have a definite syntax and semantics. A paragraph of text or a text document has several elements. Those you can further breakdown into clauses, phrases, and words. 
  • The popular tokenization techniques include sentence and word tokenization. These, you can use to break down a text document (or corpus) into sentences. And each sentence into words. 
  • Thus, tokenization is the process of breaking down or splitting textual data into smaller and more meaningful components called tokens.


Python is popular in Text analytics. Here, you will find various cleaning techniques used in text analytics.
Text Analytics in Python 



3. Removing Unnecessary Stop Words

Stopwords have little or no significance and are usually removed from a text when processing it. These usually occur most frequently if you aggregate a corpus of text based on the particular tokens and their frequencies. Words like "a," "the," "and," and so on are stopwords.


4. Handling contractions

The best examples of contractions are you'll, it's, etc.



5. Correcting spelling errors

Auto correcting spelling errors. While doing a Google search, you will find it corrects your spelling automatically.



6. Stemming

Here you can reduce words to the root level. The best example is Snowball, this you stem it to root level as Snow and Ball.



7. Lemmatization

Based on the context, bring the words to the root level, and make them meaningful.


8. Tagging

This is the concept of group particular words under a Tag.



9. Chunking

It is of constructing from various words of Verbs, Nouns, Adjectives, etc. Checkout here on Data Chunking.



10. Parsing

The data will pass through some syntax rules. The output will then feed to Machine Learning systems. The syntax rules vary from project to project.

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