Text Data Processing: Basics & Techniques

This lecture covers the fundamentals of handling text data, including document retrieval, document classification, sentiment analysis, and topic detection. It explains how to phrase these tasks as machine learning problems and preprocess text for machine learning algorithms. The lecture also delves into the challenges of working with unstructured text data, such as character encoding, language identification, tokenization, stopword removal, and word normalization. Various techniques like bag of words, n-grams, TF-IDF matrix, and normalization methods are discussed to prepare text data for machine learning applications.