Text cleaning

The primary goal of text cleaning is to transform unstructured textual information into a standardized and more manageable form. While cleaning text, several operations are commonly performed, such as the removal of HTML tags, special characters, and numerical values, as well as the standardization of letter cases and the handling of whitespaces and formatting issues. These operations collectively contribute to refining the quality of textual data and reducing its ambiguity. Let’s deep dive into these techniques.

Removing HTML tags and special characters

HTML tags are often present due to the extraction of content from web pages. These tags, such as <p>, <a>, or <div>, carry no semantic meaning in the context of NLP and must be removed. The cleaning process involves the identification and stripping of HTML tags, leaving behind only the actual words.

For this example, let’s consider a scenario where we have a dataset of user reviews...