Session 2: Traditional NLP Techniques and Text Preprocessing#
Welcome to Session 2 of our course on Natural Language Processing for Social Science. In this session, we’ll delve into the foundational techniques of NLP and the crucial preprocessing steps that form the backbone of text analysis.
Natural Language Processing (NLP) has come a long way in recent years, with advanced models like Large Language Models (LLMs) pushing the boundaries of what’s possible. However, understanding and mastering traditional NLP techniques remains crucial for several reasons:
Foundation Building: These techniques form the basis upon which more advanced methods are built.
Interpretability: Traditional methods often offer clearer insights into how text is being processed and analyzed.
Efficiency: For many tasks, especially with smaller datasets, traditional techniques can be more efficient and equally effective as more complex models.
Preprocessing Importance: Regardless of the final analysis method, proper text preprocessing is essential for achieving accurate and meaningful results.
In this session, we’ll cover three main areas:
Throughout this session, we’ll provide practical examples and code snippets to illustrate these concepts. We’ll use popular Python libraries such as NLTK, scikit-learn, and gensim to implement these techniques.
By the end of this session, you’ll have a solid understanding of the preprocessing steps necessary for any NLP task, as well as hands-on experience with several key NLP techniques. These skills will provide a strong foundation for more advanced NLP applications in social science research, which we’ll explore in later sessions.
Remember, while we’re focusing on traditional techniques in this session, the preprocessing steps and basic tasks we’ll cover remain relevant even when working with more advanced models like LLMs. A thorough understanding of these fundamentals will enhance your ability to leverage NLP effectively in your research, regardless of the specific tools or models you ultimately use.
Let’s begin our exploration of traditional NLP techniques and text preprocessing!