Module 1: Natural Language Processing - Introduction
Module Overview
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. In this module, we'll explore the foundational concepts of NLP, including text preprocessing techniques that are essential for any text-based analysis. We'll learn how to tokenize text, remove stop words, and apply stemming or lemmatization to prepare text data for more advanced NLP applications.
Learning Objectives
- Tokenize Text
- Remove Stop Words From a List of Tokens
- Stem and Lemmatize Text
Guided Project
Open DS_411_Text_Data_Lecture_GP.ipynb in the GitHub repository to follow along with the guided project.
Module Assignment
Analyze coffee shop reviews to identify attributes of the best and worst rated establishments. Apply text preprocessing techniques including tokenization, lemmatization, and custom stopword removal to clean the data and create visualizations showing token frequency patterns across different star ratings.
Assignment Solution Video
Additional Resources
NLP Libraries and Text Processing
- spaCy Linguistic Features Documentation
- NLTK Book: Processing Raw Text
- Scikit-Learn: Text Feature Extraction