Text Preprocessing

Text preprocessing is a set of techniques used to clean, normalize, and transform raw text data into a structured format suitable for analysis or machine learning models. It involves steps like tokenization, stopword removal, stemming, lemmatization, and vectorization to reduce noise and improve computational efficiency. This process is fundamental in natural language processing (NLP) to prepare text for tasks such as sentiment analysis, topic modeling, or text classification.

Also known as: Text Cleaning, Text Normalization, NLP Preprocessing, Text Data Preparation, Text Wrangling

Why learn Text Preprocessing?

Developers should learn text preprocessing when working on NLP projects, as it directly impacts model performance by handling inconsistencies like punctuation, case variations, and irrelevant words. It is essential for applications like chatbots, search engines, and document analysis, where clean input data leads to more accurate and reliable results. Without proper preprocessing, models may struggle with noise and produce suboptimal outcomes.

Learning Resources

• tutorial: NLTK Text Processing Tutorial
• docs: Scikit-learn Text Feature Extraction
• course: Coursera NLP Specialization by deeplearning.ai
• tutorial: Text Preprocessing in Python - Real Python
• docs: SpaCy Documentation on Text Processing