Lecture
This lecture covers the handling of text data, focusing on deriving clean datasets from unstructured text such as web content, social media, and news. It explores methods like bag-of-words, TF-IDF matrix, and techniques for text normalization and tokenization. The lecture delves into tasks like document retrieval, classification, sentiment analysis, and topic detection, explaining how to frame them as machine learning problems. It also discusses the importance of inverse document frequency and the challenges of working with textual data in social media. The session concludes with strategies for postprocessing the bag-of-words matrix and the significance of row and column normalization in TF-IDF matrices.