Numerical and categorical features are what we call structured data types. They are easier to process and utilize in ML workflow. Textual data is one major source of unstructured information that is equally important. Textual data presents multiple challenges related to syntactical understanding, semantics, format, and content. Textual data also presents issues of transformation into numeric form before it can be utilized by ML algorithms. Thus, feature engineering for textual data is preceded by rigorous preprocessing and clean up steps.