Lecture
This lecture covers the fundamental concepts of text data analysis, including document retrieval, classification, sentiment analysis, and topic detection. The instructor explains how to preprocess text for machine learning tasks, such as transforming text into feature vectors using bag-of-words and TF-IDF matrices. Various techniques like tokenization, stopwords removal, and word normalization are discussed. Additionally, the lecture delves into the challenges of working with unstructured text data, such as character encoding, language identification, and handling social media text. The importance of postprocessing techniques like IDF weighting and row normalization in TF-IDF matrices is highlighted, along with practical tips for improving text data analysis performance.