Regular expressions are data mining techniques applied by data scientists and analysts to define a search pattern. Regular expressions or Regex are typically applied in text mining or natural language processing, and they rely on the use of a string text to define the search pattern. Regular expressions are used in many programming platforms such as R and Python, and it specialized in manipulating text data (Brodie & et al., 2006). Regex is applied to match some pieces of text with other text, extract pieces of text that match the search expression, find pieces of text in string data and validate pieces of text in a string.

As indicated above, regular expressions go a long way in enhancing the ability of data analysts and scientists to execute tasks related to text mining. For instance, regular expressions are used to evaluate the attributes of text data before and after the mining processes. Regex provides essential details such as the sections of the text that were manipulated by the expressions, the index of the text, the beginning and the end of the section where the text matched the search pattern and the replaced portions of text within the string.

Regular expressions can be categorized into basic and extended regular expressions. Extended regular expressions are applied to match text data and are deployed in executing complex tasks. On the other hand, basic regular expressions are applied to match characters within a text. Square brackets and wildcards are examples of extended regular expressions. Square brackets are applied to match a section of unknown text by matching all the characters inside the brackets (Caron & et al., 2011). On the contrary, wildcards match single characters within a text. Wildcards are also known as dot and are applied to match a specific number of characters in a text.