Extracting Features from Text Data

Part 2 covered certain linguistic aspects, challenges in preserving semantics, understanding shallow parsing, Named Entity Recognition (NER) and introduction to language models. In this part, we seek to cover the Bag-of-Words Model and TF-IDF vectorization of text, simple feature extraction techniques that yield numeric representation of the data. A Bag-of-Words model (BoW), is a simple way of extracting features from text, representing documents in a corpus in numeric form as vectors. A bag-of-words is a vector representation of text that describes the occurrence of words in a document. It is called a'bag' of words, because any information about the order or contextual occurrence of words in the document is discarded.