Normalizing Textual Data with Python

Text normalization is the process of converting textual data into a clean and consistent format before processing it in Natural Language Processing (NLP). It helps improve text quality and makes analysis more accurate and efficient. It involves several preprocessing steps:

1. Text String

Take the input text string

Output:

"       Python 3.0, released in 2008, was a major revision of the language that is not completely backward compatible and much Python 2 code does not run unmodified on Python 3. With Python 2's end-of-life, only Python 3.6.x[30] and later are supported, with older versions still supporting e.g. Windows 7 (and old installers not restricted to 64-bit Windows)."

2. Case Conversion

Case conversion converts all text into lowercase format using the lower() method in Python.

• Converts uppercase letters to lowercase
• Improves consistency in text data
• Helps standardize similar words like “Python” and “python”

Output:

"       python 3.0, released in 2008, was a major revision of the language that is not completely backward compatible and much python 2 code does not run unmodified on python 3. with python 2's end-of-life, only python 3.6.x[30] and later are supported, with older versions still supporting e.g. windows 7 (and old installers not restricted to 64-bit windows)."

3. Removing Numbers

Removing numbers is a text normalization step used when numerical values are not important for analysis. Regular expressions (Regex) are commonly used to detect and remove numbers from text.

• Removes unnecessary numerical values from text
• Helps simplify text preprocessing
• Commonly performed using regular expressions (Regex)

Output:

"       python ., released in , was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python . with python 's end-of-life, only python ..x[] and later are supported, with older versions still supporting e.g. windows  (and old installers not restricted to -bit windows)."

4. Removing punctuation

Removing punctuation helps clean text by eliminating unnecessary symbols. Regular expressions (Regex) are commonly used to replace punctuation marks with an empty string.

• Removes punctuation symbols from text
• Simplifies text preprocessing and analysis
• Commonly performed using regular expressions (Regex)

Output:

'       python  released in  was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python  with python s endoflife only python x and later are supported with older versions still supporting eg windows  and old installers not restricted to bit windows'

5. Removing White space

Removing white spaces helps clean text by eliminating unnecessary spaces from the beginning and end of a string. In Python, the strip() function is used for this purpose.

• Removes leading and trailing spaces
• Helps clean and standardize text
• Improves text preprocessing consistency

Output:

'python  released in  was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python  with python s endoflife only python x and later are supported with older versions still supporting eg windows  and old installers not restricted to bit windows'

6. Removing Stop Words

Stop words are common words such as “the”, “is”, “a”, and “on” that usually do not carry significant meaning in text analysis. These words are commonly removed using the NLTK library during text preprocessing.

• Removes commonly used unnecessary words
• Helps focus on meaningful words in text
• Improves efficiency of NLP tasks
• Commonly performed using the NLTK library

Output:

👁 Image

In this, we can normalize the textual data using Python. Below is the complete python program:

Output:

👁 Image