The Need for Text Preprocessing

Before delving into the complexities of modeling and analysis in NLP, it's essential to understand the critical step that precedes these tasks: text preprocessing.

Raw text data is often messy and unstructured. It may contain errors, inconsistencies, slang, abbreviations, and various languages, making it challenging for NLP models to understand and process the text accurately.

Preprocessing transforms this raw text into a more manageable form, reducing noise and complexity, which enables models to perform tasks such as classification, sentiment analysis, and language translation more effectively.

Core Text Preprocessing Techniques

The text preprocessing phase encompasses several key techniques, each addressing different aspects of the text data:

• tokenization;

• cleaning and normalization;

• stop words removal;

• stemming and lemmatization;

• part-of-speech tagging.

Why NLTK?

The NLTK (Natural Language Toolkit) library is a Python library for NLP which we will actively use in our course for text preprocessing. Its intuitive design and extensive documentation cater to both beginners and experienced NLP practitioners, facilitating easy implementation of complex NLP operations.

Additionally, NLTK serves as a valuable educational resource with its rich collection of datasets and tutorials, supported by a large and active community that contributes to its continuous improvement.