This means that the more times a word appears in a document the larger its value for TF will get. Term Frequency (TF) is the number of times a word appears in a document. These two vectors and could now be be used as input into your data mining model.Ī more sophisticated way to analyse text is to use a measure called Term Frequency – Inverse Document Frequency (TF-IDF). These phrases can be broken down into the following vector representations with a simple measure of the count of the number of times each word appears in the document (phrase): Word “ The cat in the hat sat in the window“.A measure of the presence of known words.The model is only concerned with whether known words occur in the document, not where in the document. It is called a “ bag” of words, because any information about the order or structure of words in the document is discarded.

A bag of words is a representation of text as a set of independent words with no relationship to each other. When training a model or classifier to identify documents of different types a bag of words approach is a commonly used, but basic, method to help determine a document’s class. The first concept to be aware of is a Bag of Words. What, for example, if you wanted to identify a post on a social media site as cyber bullying. What do you do, however, if you want to mine text data to discover hidden insights or to predict the sentiment of the text. Machine Learning is super powerful if your data is numeric. Remove URLs, Email Addresses and Emojis.Spelling and Repeated Characters (Word Standardisation).

Clean text python